Document [original]

Nico M. Schmidt, Benjamin Blankertz, Matthias S. Treder

Online detection of error-related potentials

boosts the performance of mental

typewriters

Article, Published version

This version is available at http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-70169.

Suggested Citation

Schmidt, Nico M. ; Blankertz, Benjamin ; Treder, Matthias S. : Online detection of error-related

potentials boosts the performance of mental typewriters. - In: BMC Neuroscience. - ISSN 1471-2202

(online). - 13 (2012), art. 19. - doi:10.1186/1471-2202-13-19.

This work is licensed under a CC BY 2.0 License (Creative

Commons Attribution 2.0 Generic). For more information see

http://creativecommons.org/licenses/by/2.0.

RESEARCH ARTICLE Open Access

Online detection of error-related potentials

boosts the performance of mental typewriters

Nico M Schmidt

1,2*

, Benjamin Blankertz

and Matthias S Treder

Abstract

Background: Increasing the communication speed of brain-computer interfaces (BCIs) is a major aim of current

BCI-research. The idea to automatically detect error-related potentials (ErrPs) in order to veto erroneous decisions of

a BCI has been existing for more than one decade, but this approach was so far little investigated in online mode.

Methods: In our study with eleven participants, an ErrP detection mechanism was implemented in an

electroencephalography (EEG) based gaze-independent visual speller.

Results: Single-trial ErrPs were detected with a mean accuracy of 89.1% (AUC 0.90). The spelling speed was

increased on average by 49.0% using ErrP detection. The improvement in spelling speed due to error detection

was largest for participants with low spelling accuracy.

Conclusion: The performance of BCIs can be increased by using an automatic error detection mechanism. The

benefit for patients with motor disorders is potentially high since they often have rather low spelling accuracies

compared to healthy people.

Keywords: Brain-computer interface, Electroencephalography, ERP-Speller, Error-related potentials, Information

transfer rate

Background

Brain-computer interfaces (BCIs) establish a direct com-

munication link between the human brain and an elec-

tronic device [1,2]. The intent of the user is ‘decoded’

from her/his brain signals, e.g. from electroencephalo-

graphy (EEG) or magnetoencephalography (MEG), and

transformed into control commands for an external

device. A great amount of research focuses on restoring

sensory-motor functionality or communication ability in

people who suffer from motor disorders, such as amyo-

trophic lateral sclerosis (ALS) [3]. For ALS patients, BCI

is a promising technology [4], because it can restore

their ability to communicate wishes and needs and to

interact with their environment, e.g. by controlling a

spelling application [5,6], a PC-cursor [7], or a wheel-

chair [8].

In EEG-based BCIs, many approaches capitalize on

event-related potentials (ERPs) that arise as a response

to sensory stimulation. An often targeted ERP compo-

nent is the P300, a positive deflection at central and par-

ietal electrode sites about 300 ms after the presentation

of a stimulus that the user is attending to. The P300

and other ERP components have been successfully used

as features in BCI spelling applications in order to iden-

tify the characters the user intends to write. The classic

spelling application is the so-called P300-speller intro-

duced by Farwell and Donchin [9], which is denoted

here more specifically as Matrix Speller. It consists of a

6 × 6 matrix of characters. Each row and column is

intensified (flashed) briefly in a random order, while the

user is directing her/his gaze to the target character.

Since detecting the P300 in single trials is intricate, the

intensification sequence is repeated several times. By

optimizing the number of sequence repetitions, the

duration of the flashes, as well as the classification

methods, a spelling speed of up to 5.8 characters per

minute has been reported [10].

Compared to alternative technologies such as eye-

trackers or EOG-based systems, where users communi-

cate with up to 10 words per minute [11], this spelling

* Correspondence: [email protected]

Machine Learning Laboratory, Berlin Institute of Technology, Berlin,

Germany

Full list of author information is available at the end of the article

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in

any medium, provided the original work is properly cited.

speed is rather low. Therefore, currently, the clinical

application of BCI spellers is mainly of interest in cases

of severe oculomotor impairment. It has been shown

however, that the spelling accuracy of the Matrix Speller

also depends on the user’s capability to direct her/his

eye gaze to the desired target character. The accuracy

drops critically low when the user is required to fixate a

dot in the center of the matrix with her/his eyes [12,13].

Recently, some novel approaches for visual spellers

have been proposed to overcome this restriction [14-16].

Our study builds on the so-called Center Speller [15,17],

but the method could similarly be applied to other spel-

lers. The Center Speller is a visual ERP-speller, which

uses a two-step selection process: first, six groups of five

characters are presented one by one in a fast sequence

in the center of the screen. The user is attending to the

target group, i.e. is waiting for its appearance. In the

second step, the characters of the previously selected

group are presented in the same way. In both steps, the

six choices are coupled to simple geometric shapes of

unique colors in order to facilitate the allocation of

attention in fast stimulus sequences (see [15] and

method section for a more detailed description).

As mentioned above, a bottleneck of current state-of-

the-art BCIs is the low information throughput. For the

Center Speller, a previous study showed an average spel-

ling speed of about 1.5 characters/minute at 10

sequence repetitions (i.e. each of the six groups/charac-

ters is presented 10 times) [15]. Several approaches have

been explored in order to increase communication

speed. One possibility is to reduce the number of repeti-

tions, at the risk of decreasing spelling accuracy and

fatigue of the participant. An optimal balance between

the number of repetitions and accuracy can be achieved

by means of a dynamic stopping method that statisti-

cally evaluates the confidence of the classification after

each intensification sequence. If the classifier is confi-

dent about the selection, the presentation sequence is

stopped [18-20]. Another factor affecting communica-

tion speed is experimental overhead. In the Center Spel-

ler, the selection process for each character begins with

a countdown before the sequence presentation starts.

Furthermore, it contains a few animations and presenta-

tion of the selected character (feedback). Spelling speed

can be increased by reducing the durations of count-

down, feedback and animations. As with reduction of

repetitions, a potential drawback in reducing the over-

head is that a too-short spelling process could be

exhausting to the user because it may require more

attention.

A different to increasing the spelling speed is the

detection of error-related potentials (ErrPs). ErrPs are

a certain type of ERPs that are present in the EEG sig-

nals when the user is aware of erroneous behavior.

ErrPs probably arise in the anterior cingulate cortex, a

brain area involved in processing of emotion and

attention, and are thus found over central and prefron-

tal electrode positions [21]. They are characterized by

an early negative voltage deflection over fronto-central

regions, referred to as error-negativity (N

) or error-

related negativity, followed by a positive deflection

over parietal regions, referred to as error-positivity (P

)

[22]. The characteristics of the ErrPs vary, depending

on the situation in which the erroneous behavior was

perceived. In errors during a choice reaction task,

where the subjects respond to a stimulus by pressing a

button, erroneous button presses yield ErrPs that are

sometimes referred to as “response ErrPs”.TheN

appears after 80 ms, the larger P

follows around 200-

500 ms relative to the button press [23,24]. When

users perform wrong in a reinforcement learning task

and receive a feedback indicating the wrong action, the

observed main component is the N

around 250 ms

after the stimulus and this is referred to as “feedback

ErrP”[21]. When users observe erroneous behavior of

other persons, the so-called “observation ErrP”appears

to be similar to the feedback ErrP [25]. In BCI experi-

ments the situation is different. Errors are usually

neither caused by the user’s action nor by another per-

son the user is observing but by the misclassification

oftheBCI.Interestingly,inthiscaseErrPsalsoarise,

with an N

component after 270 ms and a larger P

component 350-450 ms after the appearance of the

BCI’s feedback [26-31]. Ferrez and Millán [26] coined

the term “interaction ErrP”for this type of ErrP.

Few studies have been conducted so far on the detec-

tion of interaction ErrPs. ErrP detection has been used

to detect error trials offline in EEG-data of motor ima-

gery experiments [32], in EEG-data of button press

experiments with artificially induced errors [28], in

MEG-data of covert attention experiments [33], as well

as in EEG-data of Matrix Speller experiments [31]. Dal

Seno et. al [34] used online ErrP detection in pseudo-

online Matrix Speller experiments with five healthy par-

ticipants, and later in online Matrix Speller experiments

with three participants [30]. Spüler [29] showed success-

ful online ErrP detection with the Matrix Speller in 12

healthy participants (29.5%increaseofbitrate)and4

patients with motor disorders (35.6% increase of bit

rate).

The aim of the present study was to investigate,

whether the communication rate of gaze-independent

BCIs can be increased using online detection of ErrPs.

To this end, an error detection mechanism was imple-

mented in the Center Speller. If an error potential was

detected by the ErrP classifier upon presentation of

the classified symbol, the selection was vetoed and the

trial was restarted. The communication rate in

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 2 of 13

characters/minute of this modified speller was then

compared to the communication rate of the Center

Speller without error detection. Moreover, two differ-

ent ErrP classifiers were compared; one classifier was

trained on Center Speller data and another one was

trained on data of a calibration experiment and was

then applied in the Center Speller experiment. In the

Methods section, both classifiers are introduced and

the experimental protocol is explicated. In the Results

section, we report on the neurophysiological data and

on the impact of error potential detection on commu-

nication rate.

Methods

Center speller

The selection process in the Center Speller (see Figure 1)

is split into two levels: Thirty characters are divided into

six groups of five characters each. In the first level, the

six groups (ABCDE, FGHIJ, KLMNO, PQRST, UVWXY

and Z_., <) are presented several times one by one in a

random order for 100 ms with 100 ms inter-stimulus

interval (total stimulus onset asynchrony is 200 ms). The

group containing the target character has to be selected

by attending to it. In the second step, the single charac-

ters from the previously selected group are presented in

the same way. Since six selectable stimuli are presented

in a random order with equal frequency, the presentation

of the target stimulus constitutes a rare event and is sup-

posed to modulate the ERP. Each group (in level one)

and each character (in level two) is placed on a polygon

with an unique geometric shape and color (red triangle,

green bar, blue bar, yellow downward triangle, magenta

hourglass and white circle). This way, the two visual fea-

tures, color and form, are additionally provided by the

stimuli.

After having presented all stimulus sequences of a

level, the feedback indicating the selected group or char-

acter is presented for 1s. In case of a wrong selection by

the classifier, an ErrP is elicited and can be detected by

another classifier. If this classifier detects an ErrP, a red

‘X’appears over the feedback to indicate that the selec-

tion by the group or character is vetoed and the stimu-

lus sequence is immediately repeated.

For the case that a wrong group was selected but no

ErrP could be detected, the character level provides a

backdoor indicated by an accent character”.Byselecting

the backdoor, one returns to the group level without

spelling a character. If a wrong selection occurred at the

character level, a correction can be made via the less-

than symbol ‘<‘, which serves as a backspace.

Figure 1 Program flow of the Center Speller with ErrP detection. Spelling starts with a countdown (a), followed by the stimulation

sequence (b), wherein each symbol is presented sequentially in a randomized order. Then, the feedback (selected symbol group) is presented to

the user (c), and the ErrP classifier evaluates the brain response (ErrP present/absent). If an ErrP is detected, a red ‘X’indicates the error (d) and

the stimulus sequence repeats. Otherwise, the algorithm proceeds with level 2 (e) wherein the selection process is repeated on the single

character level (f-h). Finally, the selected character is appended to the phrase shown at the top of the screen and the next trial is started.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 3 of 13

ErrP calibration speller

In order to train an ErrP classifier, a sufficient number

of trials needs to be collected. However, the Center

Speller yields a spelling speed of about two characters

per minute, i.e. four samples of feedback evaluation per

minute. In other words, to obtain a moderate training

set for the ErrP detection, participants need to engage

in a long spelling session. Depending on the desired size

of the training set, this could exceed the reasonable

duration of an experiment.

As an alternative, a calibration experiment was designed,

wherein the participants spell via key press in a much fas-

ter way than using a BCI. The spelling process and the

appearance of this experiment were designed to be as

similar as possible to those of the Center Speller, in order

to assure that the classifier trained on this data transfers to

the Center Speller application. Just as in the Center Spel-

ler, the ErrP Calibration Speller features two levels, one to

select the target group, the other one to select the target

character. Instead of presenting the elements sequentially

in the center of the screen, they are shown in a hexagonal

arrangement (Figure 2). A small arrow in the center of the

screen is used to select the target. The arrow continuously

rotates clockwise with a speed of 240 deg/s and the parti-

cipant has to press a key when the arrow points on the

target symbol. As soon as the key is pressed, feedback

identical to the Center Speller feedback is presented: a

fixation point is visible for one second, followed by the

one second lasting presenting of the selected group or

character. By waiting for one second between key press

and feedback presentation, muscular influence on the

feedback-ERPs is prevented.

In order to obtain error potentials of the type interac-

tion-ErrP, artificial errors were induced. In these cases,

the element located on the side opposite to the selected

one was selected instead. Choosing the symbol at the

opposite side of the screen guaranteed that the partici-

pant did not misperceive the interaction error as her/his

own error (own errors occurred when the participant

hit the key while the arrow was not pointing on the tar-

get, i.e. too early or too late).

Procedure

The course of the experimental session is depicted in Fig-

ure 3. Prior to the experiment, participants were

instructed to relax their muscles and to avoid eye blinks

and eye movements during the trials. For the Calibration

Speller experiments, participants were asked to place

their dominant hand on the keyboard in a relaxed posi-

tion and press the key with the index finger only. The

session started with one block of speller training (Center

Speller in offline mode with 10 sequence repetitions).

During training, the spelling process was predefined and

the participant had to attend the indicated symbols while

EEG was recorded for offline analysis. Subsequently, the

ErrP Calibration Speller experiment was performed in

fixed-spelling mode with 15% artificial errors. In fixed-

spelling mode, the participant had to copy a given text

and correct all errors that occurred during the spelling

process. Based on these two calibration blocks, the spel-

ling classifier and ‘ErrP classifier A’were trained.

Furthermore, the bias of the ErrP classifier was adjusted

to have a false alarm rate of at most 5%. The false alarm

rate indicates how many trials were classified as being

wrong although the selection was correct, whereas the hit

rate indicates the fraction of successfully detected erro-

neous trials. At the same time, the number of sequence

repetitions was adjusted to obtain a spelling accuracy

close to, but higher than, 70%. Nine online spelling

blocks (Center Speller in fixed-spelling mode) were per-

formed. After completion of the fourth block, another

ErrP classifier was trained on the online data, referred to

as ‘ErrP classifier B’. In other words, there were three

spelling conditions during the nine online blocks: spelling

without ErrP detection, spelling with ErrP detection

using classifier A (trained on the calibration data) and

spelling with ErrP detection using classifier B (trained on

the online spelling data). The order of the conditions was

the same for all participants (as shown in Figure 3).

Participants

Twelve participants (7 males and 5 females), aged 23-31

years (μ= 26), participated in the study. One participant

Figure 2 Calibration Experiment. The central arrow rotates

clockwise and the participant has to press a key when the arrow

passes the target symbol. The visual feedback is identical with the

feedback of the Center Speller.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 4 of 13

was excluded due to lack of BCI-control. The spelling

accuracy for this participant was below 50% which made

it impossible to spell and the experiment was aborted.

All but one participant were right-handed and all had

normal or corrected-to-normal visual acuity. Normal

color vision in all but one participant (iac)wascon-

firmed using the Ishihara color vision test [35]. All parti-

cipants gave written consent and the study was

performed in accordance with the Declaration of

Helsinki.

Apparatus

EEG was recorded using a Brain Products (Munich,

Germany) actiCAP active electrode system with 64 elec-

trodes and Brain Amp amplifiers sampling at a rate of

1000 Hz. The electrodes were placed according to the

international 10-10 system at positions Fp1,2, AF3,4,7,8,

Fz, F1-10, FCz, FC1-6, FT7,8, T7,8, Cz, C1-6, TP7,8,

CPz, CP1-6, Pz, P1-10, POz, PO3,4,7,8, Oz and O1,2.

One electrode was placed under the right eye and

labeled as EOGvu. Active electrodes were referenced to

a nose electrode, using forehead ground. Impedances

were kept below 15 kΩ. EEG signals were hardware fil-

tered at 0.016-250 Hz. The stimuli were presented on a

24”TFT screen with a resolution of 1920 × 1200px

and a refresh rate of 60 Hz. Participants were seated at

70 cm distance from the screen. To correct the EEG

markers for the TFT latency, a photo diode (g.TRIGbox;

g.tec medical engineering, Graz, Austria) was attached

to the lower left corner of the screen for the first six

experiments, registering the exact stimulus onset. The

median TFT latencies over the six experiments range

from 69 ms to 71 ms and for offline analysis, the mean

value of 69.8 ms was added to the EEG marker times of

all experiments. The Center Speller and the Calibration

Speller were implemented in Python http://www.python.

org using the open-source-framework Pyff [36] with

VisionEgg [37] and Pygame http://www.pygame.org.

Remote-controlling of the experiments, online classifica-

tion as well as offline analysis was done with an inhouse

toolbox using MATLAB (The MathWorks, Natick, MA,

USA). The Center Speller is freely available in the Pyff

repository (see http://bbci.de/pyff).

Data analysis

For online classification, the EEG data was down-

sampled to 100 Hz and baseline corrected for the 200

ms prestimulus interval (both, for the speller stimuli and

for the feedbacks, which form the ErrP stimuli).

For offline analysis, the data was lowpass filtered using

a Chebyshev filter with 42 Hz passband and 49 Hz stop-

band and then downsampled to 250 Hz. The continuous

signal was divided into epochs ranging from -200 ms to

1200 ms relative to the onset of the stimulus and epochs

were baseline corrected for the 200 ms prestimulus

interval. Artifacts were detected to account for eye

blinks, eye movements, muscular activity and malfunc-

tioning hardware. Trials and channels containing such

artifacts were rejected for visual ERP analysis, but not

for classification purposes. The artifact detection was

done using a variance criterion, i.e. channels and trials

with too low or too high voltage variance were labeled

as contaminated by artifacts, as well as using a min-max

criterion, i.e. all trials in which the difference between

maximum and minimum voltage exceeds 75 μVwere

labeled as contaminated by artifacts. In the ErrP Calibra-

tion Speller experiments, all trials containing errors

made by the participant were also rejected.

Thesignedsquareofthepoint-biserial correlation

coefficient sgn r

was used for ERP analysis as a measure





















 !"

Figure 3 Session Design. In total, the experiment consists of 11 blocks. First, the Center Speller is run in offline mode (18 characters), then the

ErrP Calibration Speller is run in fixed-spelling mode (83 characters). These two blocks (gray) are used to train the spelling classifier and the ErrP

classifier A, respectively. Subsequently, 4 Center Speller blocks are performed in fixed-spelling mode (20 characters per block), and the data is

used to train ErrP classifier B. Then the last 5 Center Speller blocks are performed. The three different conditions have been interleaved during

the 9 blocks: spelling without ErrP detection (black), spelling with ErrP detection using ErrP classifier A (magenta) and spelling with ErrP

detection using ErrP classifier B (blue).

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 5 of 13

for the discriminability of two classes (target vs. non-tar-

get or error vs. non-error) [38].

The performance of the spelling classifier (i.e.classifi-

cation accuracy) is given as the percentage of correctly

selected symbols (per level). The performance of the

ErrP classifiers is given in terms of the receiver-operat-

ing-characteristic (ROC). An ROC curve can be depicted

as a plot of false alarms against hits. Since a good classi-

fier allows for a high hit rate at few false alarms, the

area under the ROC curve (AUC) is a commonly used

quantification of classifier performance. Furthermore,

the accuracy of the ErrP detection is split into hits (an

error trial classified as error) and false alarms (a correct

trial classified as error), or their respective rates for

some analyses. The spelling speed is given in terms of

the number of characters that were spelled per minute,

abbreviated char/min.

Classification

All classifiers, that is, the spelling classifier (used for

detecting the target symbol) and the two ErrP classifiers

(used for detecting ErrPs upon presentation of the feed-

back), used spatio-temporal features for a linear discri-

minant analysis with shrinkage of the covariance matrix

(see e.g. [38]). In the spatial domain, all electrodes

except for Fp1,2, AF3,4,7,8 and EOGvu were considered

(57 channels) for online classification. In the temporal

domain, a heuristic method [38] was used, searching for

peaks in the sgn r

between targets and non-targets in

the 100-700 ms post-stimulus interval (for the spelling),

and between errors and non-errors in the 150-900 ms

post-feedback interval (for the ErrP detection), respec-

tively. The heuristic method initially determined 5 tem-

poral intervals, but the number of intervals and the

exacttemporalpositionofthemcouldbeadjustedby

the experimenter before the online operation was

started. Finally, the voltages of all selected electrodes

were averaged within the selected intervals, constituting

a feature vector of length d = nIvals nElectrodes (d =

285 in case of nIvals = 5 intervals and nElectrodes = 57

electrodes).

In an offline analysis, the spatial distribution of the

class-discriminative information for ErrP detection was

investigated by training one classifier individually for

each electrode channel [38]. For each channel, four time

intervals were chosen automatically by selecting peaks

in the sgn r

values. Voltages were then averaged within

the respective intervals resulting in four dimensional

features. Training and test sets were chosen in the same

two ways as in the online experiments, relating to classi-

fier A and to classifier B. We refer to these classifiers as

type-A and type-B classifiers.

To investigate whether artifacts from eye blinks or

raised eyebrows could explain the classification results,

classification was repeated offline for frontal electrodes

(Fp2, F9, F10, EOGvu) that were most susceptible to

ocular artifacts.

Results

Symbol selection

ERPs

Figure 4 shows the grand-average event-related poten-

tials (ERPs) that are related to the presentation of the

target and non-target stimuli during the spelling process

with the Center Speller. The potentials show an oscillat-

ing pattern with a phase length equal to the stimulus

onset asynchrony of 200 ms. Due to this short stimulus

onset asynchrony, the ERPs of successive presentations

overlapped substantially. A negativation at 200-280 ms

was present over occipital regions, referred to as N200.

This negativation was stronger in the target condition

than in the non-target condition and led to a peak in

the sgn r

values over left inferotemporal regions. A

large positivation in the target condition, the P300 com-

ponent, was found over central regions 300-500 ms after

stimulus onset. The positivation was absent in the non-

targets which leaded to high sgn r

values. The P300

reflects the recognition of the rare target event the parti-

cipant was attending to.

Classification

The number of sequence repetitions was set to values

between 1 and 7 (μ= 2.9, see Table 1). Classification

accuracy for levelwise selection (chance level 16.67%)

varied significantly between the group level (75.2% ±

3.1) and single character level (82.7% ± 2.4, t = 7.81, p

<0.001,see3

column of Table 1). The recognition of

asinglecharactertargetisapparently easier than the

recognition of a group target consisting of five small

characters. The three conditions (spelling without ErrP

detection, spelling with ErrP detection with classifier A

and B) showed no difference in accuracy of the spel-

ling classifier. A one-way analysis of variance

(ANOVA) yielded no significant effect of experimental

condition (p= .87).

ErrP detection

ERPs

The grand-average ERPs with respect to the feedback

presentation of error and non-error trials, the error-

related potentials, are depicted in Figure 5 for the ErrP

Calibration Speller (left) and for the Center Speller

experiments (right). In the Center Speller, the responses

of both conditions were up to 7 V higher than in the

ErrP Calibration Speller. Also, the sgn r

reached higher

values in the Center Speller [-0.1, 0.05] than in the ErrP

Calibration Speller [-0.025, 0.035]. In both spellers, the

error condition (red lines) deviated strongly from the

non-error condition (green lines). In the sgn r

values

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 6 of 13

−100 0 100 200 300 400 500 600 700 800

−5

Cz (thick) PO7 (thin)

[ms]

[μV]

target

nontarget

−100 0 100 200 300 400 500 600 700 800

−0.02

0.02

Cz (thick) PO7 (thin)

[

]

sgn r

sgn r2

[μV]

−4

−3

−2

−1

205 − 280 [ms] 290 − 420 [ms]

n r2]

−0.02

0.02

targetnontarget

sgn r2

Figure 4 Event-Related Potentials. Grand-average of the event-related potentials for target and non-target conditions in the Center Speller

experiment. The top plot on the left shows the voltage trace at electrode Cz (thick line) and PO7 (thin line) for the target (orange) and the non-

target (dark gray) condition, the bottom plot shows the time evolution of the sgn r

values indicating the target/non-target class difference at

those electrodes. The average voltages and sgn r

values of the two intervals (indicated by the gray patches), are shown as scalp topographies

on the right side.

Table 1 Results of the online study.

Participant Nr. Spelling without Training Set Performance Training Set Performance

code Rep. ErrP detection of Classifier A of Classifier A of Classifier B of Classifier B

(Accuracy l1, l2/Speed) (Trials/Errors) (AUC/Speed) (Trials/Errors) (AUC/Speed)

gbo 2 64.7%, 73.7%/0.9 317/47 (14.8%) 0.83/1.5 364/114 (31.3%) 0.97/2.4

bad 2 64.4%, 72.7%/1.2 294/45 (15.3%) 0.85/1.6 481/145 (30.1%) 0.97/2.3

iae 1 66.3%, 75.8%/1.3 309/50 (16.2%) 0.75/2.0 315/62 (19.7%) 0.96/2.8

gbq 2 67.5%, 76.1%/1.2 315/51 (16.2%) 0.62/1.2 402/112 (30.3%) 0.96/2.4

gbt 4 84.1%, 89.1%/2.3 370/59 (15.9%) 0.80/1.8 230/26 (11.3%) 0.93/1.9

iac 2 76.1%, 82.8%/1.7 285/45 (15.8%) 0.83/2.3 291/64 (22.0%) 0.91/2.4

gbn 2 76.8%, 86.6%/1.9 330/51 (15.5%) 0.82/2.4 287/57 (19.9%) 0.87/2.5

gbw 7 81.6%, 85.6%/1.3 415/63 (15.2%) 0.87/1.4 255/35 (13.7%) 0.84/1.3

iau 3 75.3%, 85.8%/1.9 364/60 (16.5%) 0.69/1.6 224/49 (21.9%) 0.79/2.2

mk 3 72.1%, 83.0%/1.9 308/46 (14.9%) 0.69/1.5 290/50 (17.2%) 0.75/1.5

fat 4 98.5%, 99.0%/2.7 379/60 (15.8%) 0.95/2.6 169/2 (1.2%) 0.99/2.7

Mean 2.9 75.2%, 82.7%/1.7 335/52 (15.6%) 0.79/1.8 301/66 (19.9%) 0.90/2.2

SE 0.5 3.1, 2.4/0.2 27/4 (1.3) 0.03/0.1 27/13 (2.7) 0.02/0.1

Column 2: The number of sequence repetitions used for the Center Speller. Column 3: Overall spelling accuracies for each of the two levels and the spelling

speed for spelling without ErrP detection. Columns 4 and 6: The number of training samples and the proportion of error trials for classifier A and B, respectively.

Columns 5 and 7: The ErrP detection performance of the two classifiers as area under the ROC curve, as well as the spelling speed in char/min when using the

two classifiers, respectively

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 7 of 13

this was reflected by a negativation 200-350 ms after

onset of the feedback, referred to as error negativity

),whichwasnotonlymuchstrongerintheCenter

Speller experiments, but also appeared about 40 ms ear-

lier than in the Calibration Speller. The N

appears first

spread across central, temporal and parietal areas, with

a peak over central electrode sites and then persisted

only over occipital regions with a peak over the right

side (e.g.electrodePO8).TheN

was followed by a

large positivation 350-800 ms after feedback onset, the

error positivity (P

). It appeared first centrally and then

moved to centro-parietal regions. Again, the P

reached

much higher values in the Center Speller and it reached

its maximum faster (after 380 ms) than in the ErrP Cali-

bration Speller (maximum after 650 ms). The spatio-

temporal characteristics of N

and P

were in accor-

dance with these found by Combaz et. al [31]. The

ErrPs observed by Ferrez and Millán [28] looked differ-

ent, because the EEG signals were processed in different

ways. However, by applying a 1-10 Hz bandpass filter as

they did and by using the miss-minus-hit curve instead

of the sgn r

, we found similar ErrPs, except for a small

time shift (data not shown). This time shift could be

due to the monitor latency or the phase shift caused by

the bandpass filtering, which might differ in the two

settings.

Classification

The results of the ErrP classification are summarized in

Table 1. The number of training samples for the two

classifiers are shown in column 4 and 6, respectively.

For classifier A, on average 335 trials, 15.6% of which

were errors, were available for training. Classifier B had

with 301 trials and 19.9% errors a similar average train-

ing set size. For all participants, the performance in

terms of the area under the ROC curve was above 0.62

for classifier A and above 0.75 for classifier B. Figure 6

shows the ROC curves for each participant and classi-

fier, as well as the respective AUCs, hit rates and false

alarm rates. The performance of classifier B (blue bars)

was higher (mean 0.90 AUC) than the performance of

classifier A (magenta bars, mean 0.79 AUC) for all but

one participant (gbw). A paired-samples t-test on the

performance of both classifiers confirms this difference

(t = 3.8, P < 0.01). The higher performance of classifier

B had its origin in the higher hit rates, whereas the false

alarm rates were similar for both classifiers (approx.

Figure 5 Error-Related Potentials. Grand-average of the event-related potentials for error and non-error conditions in the ErrP Calibration

Speller (left side) and in the Center Speller (right side) experiment. The top rows show the voltage trace at electrode Cz (thick lines) and PO7

(thin lines) for the error (red) and non-error (green) conditions. The average voltages of five intervals (depicted by the gray patches), are shown

as scalp topographies in the two rows below. The third row shows the scalp topographies of the sgn r

values, averaged in these intervals. The

bottom plot shows the time evolution of the sgn r

values for electrode Cz and PO7. In the scalp maps, electrodes Cz and PO7 are marked with

thick crosses.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 8 of 13

5%). For participants gbt and iac however, the false

alarm rates reached almost 10%, although the classifier

was biased in order to keep the false alarm rates below

5%. The ROC curves confirm the advantage of classifier

B in reaching higher hit rates at lower false alarm rates

in this classifier. The performance of classifier A was

not significantly different (t=1.2,P=0.25) in the

online experiments on the blocks 4, 6 and 7, compared

to the performance it achieved in an offline analysis on

the blocks 8, 10 and 11 (the blocks where classifier B

was used during the online experiments). Thus the

advantage of classifier B cannot be explained by an

learning effect of the participant.

Spelling speed improvement

Figure 7 compares the spelling speed of the three condi-

tions: Spelling without ErrP detection, spelling with ErrP

detection using classifier A and spelling with ErrP detec-

tion using classifier B. For most participants the spelling

speed increased when using classifier A and increased

even more when using classifier B. For two participants

(gbw and fat) the speed did not change remarkably.

Two other participants (gbt and mk) showed even a

reduced speed when using ErrP detection. The average

spelling speed was the highest for classifier B with 2.2

char/min. An ANOVA revealed a difference in spelling

speed between the three conditions (F=3.89,p<0.05).

gbo bad iae gbq gbt iac gbn gbw iau mk fat Mean

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

area under ROC curve (AUC)

participant

ErrP−Detection Accuracy

ErrP classifier A

ErrP classifier B

gbo bad iae gbq gbt iac gbn gbw iau mk fat Mean

0.2

0.4

0.6

0.8

hit rate

Hits / False Alarms

gbo bad iae gbq gbt iac gbn gbw iau mk fat Mean

0.05

0.1

0.15

0.2

artici

ant

false alarm rate

ErrP classifier A

ErrP classifier B

0 0.2 0.4 0.6 0.8 1

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

false alarm rate

hit rate

classifier A

gbo / auc: 0.83

bad / auc: 0.85

iae / auc: 0.75

gbq / auc: 0.62

gbt / auc: 0.8

iac / auc: 0.83

gbn / auc: 0.82

gbw / auc: 0.87

iau / auc: 0.69

mk / auc: 0.69

fat / auc: 0.95

0 0.2 0.4 0.6 0.8 1

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

false alarm rate

hit rate

ROC classifier B

gbo / auc: 0.97

bad / auc: 0.97

iae / auc: 0.96

gbq / auc: 0.96

gbt / auc: 0.93

iac / auc: 0.91

gbn / auc: 0.87

gbw / auc: 0.84

iau / auc: 0.79

mk / auc: 0.75

fat / auc: 0.99

Figure 6 ErrP Classification. Performance of the online ErrP classifiers. The AUC for each participant and for the mean are depicted in the

top left plot for classifier A (magenta) and classifier B (blue). The hit rates and false alarm rates for each participant and classifier are shown

underneath. The right plots show the ROC curves of each participant for classifier A (top) and classifier B (bottom). Classifier B has higher

AUCs in all but one participant. The mean AUCs differ more than 10%. This can also be seen in the ROC curves, which reach higher hit rates

at lower false alarm rates for classifier B. False alarm rates could be kept around 5% in most participants. In two participants, the false alarm

rate approached 10%.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 9 of 13

Tukey-Kramer post-hoc tests showed that spelling speed

for classifier B was significantly higher than spelling

speed without ErrP detection. The spelling speed

obtained with classifier A did not differ significantly

from the speed in the other conditions.

Figure 8 depicts the relationship between the spelling

speed and the spelling accuracy for the three conditions.

Not surprisingly, spelling speed was low when spelling

accuracy was low, and vice versa. Using ErrP detection,

however, the effect of spelling accuracy on speed was

attenuated. In case of classifier B (blue line), the mean

spelling speed was above 2 char/min even at an spelling

accuracy of only 65%, which was more than twice as

fast than without ErrP detection (black line). At high

accuracies, however, false alarms outweighed the hits, so

that fastest spelling was obtained without ErrP detection

(2.75 char/min compared to approx. 2.5 char/min for

classifiers A and B). Classifier B became advantageous

where the black and the blue lines cross, at 95% spelling

accuracy. Classifier A (magenta line) became advanta-

geous only below 90% spelling accuracy (crossing of the

black and the magenta lines).

Spatial distribution of discriminative information

The spatial distribution of the class-discriminative infor-

mation for ErrP detection is shown as scalp topographies

in Figure 9 (electrode EOGvu, which was placed below

the right eye was included in the scalp maps). As in the

online experiments, type-A classifiers yielded a lower

overall performance compared to type-B classifiers, with

peak performance at 0.67 and 0.8, respectively. The per-

formance of the two types of classifiers had a similar spa-

tial distribution as the ErrP components themselves. For

type-A classifiers, the highest performance was obtained

over central regions, with a bias to the right hemisphere

(peak performance at electrodes C2 and FC4). For type-B

classifiers, the peak performance was found for electrode

Cz and was decreasing towards the periphery.

No ErrP detection ErrP classifier A ErrP classifier B

0.5

1.5

2.5

p < 0.05p = 0.25

p < 0.05

characters / minute

pelling

peed

gbo

bad

iae

gbq

gbt

iac

gbn

gbw

iau

fat

Figure 7 Spelling Speed. Spelling Speed in the three conditions: without ErrP detection (left), with ErrP classifier A (middle) and with ErrP

classifier B (right). The gray error bars give the mean speed and standard error for each condition. The double arrows indicate the three paired-

samples t-tests with the respective pvalues.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 10 of 13

ErrP detection performance was lower for frontal elec-

trodes (Fp2, F9, F10, EOGvu) than for all other electro-

des (type-A: t= 2.89, p< 0.05; type-B: t=4.42,p

<0.01). This is in line with Figure 9, where classification

performance is at a minimum for frontal channels.

These results suggests that ocular artifacts are unlikely

to substantially contribute to successful error detection.

Discussion

Single-trial ErrPs were detected with a mean accuracy of

89.1% (AUC 0.90). The online detection rate was similar

to the cross-validation results in offline studies, where

82% [28] and 80% [34] have been reported. ErrP detec-

tion using a classifier trained on the online data

increased the mean spelling speed by 49.0% compared

to the case without ErrP detection. A similar rate of

improvement was obtained by [29] with ErrP detection

in Matrix Speller experiments (29.5% increase of the bit

rate). This illustrates that ERP spellers can be enhanced

significantly by detecting and vetoing erroneous deci-

sions of the BCI based on error potentials. Furthermore,

the gain in communication speed was relatively higher

for participants with a medium or low BCI performance

(say, > 10% errors) than for participants with a high BCI

performance.

In some cases, ErrP detection could impede spelling

speed instead of accelerating it. False alarms prolonged

the spelling process because a correct selection was

vetoed and had to be repeated. In particular, in cases

where participants produced few errors (due to high

spelling accuracy), the potential of improvement due to

error detection was limited and could easily be out-

weighed by the detrimental effect of false alarms. This

shows that the balance between hits and false alarms of

the ErrP classifier has a crucial influence on the overall

spelling performance in terms of speed. By moving the

decision boundary of the ErrP classifier (ErrP bias), this

balance can be controlled by the experimenter. Hence,

the trade-off that maximizes communication speed is

not only a function of the number of repetitions (which

affects the spelling accuracy and thus the speed), but is

also affected by the placement of the ErrP bias. Due to

the recursive nature of the speller paradigm (the ErrP

detector can potentially veto every trial and lead to an

infinite loop), finding the optimal trade-off is an intri-

cate problem that will be addressed in future theoretical

55 60 65 70 75 80 85 90 95 100

0.5

1.5

2.5

levelwise s

ellin

accurac

[

]

aracters

nute

pelling

peed vs. Accuracy

No ErrP detection

ErrP classifier A

ErrP classifier B

Figure 8 Speed vs. Accuracy. Scatter plot of the spelling speed in dependence of the levelwise spelling accuracy. Each data point refers to

one experimental block of one participant (nine data points per participant). The blocks in which the ErrP detection was turned off are depicted

as black circles, the blocks in which classifier A was used to detect ErrPs are depicted as magenta crosses and the blocks of classifier B are

depicted as blue diamonds. For each condition a line was fitted to the data using least squares. The error bars depict the standard deviation of

the accuracy in the bands 100-90%, 90-80%, 80-70% and 70-60%.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 11 of 13

work. One approach to be mentioned here could be to

use estimates of the spelling accuracy for different repe-

tition numbers, together with estimates of the hit rate

and false alarm rate of the ErrP classifier for different

bias values (both could be obtained e.g. from the calibra-

tion blocks). Knowing the duration of a Center Speller

trial, one could simulate the spelling process for differ-

ent values of repetition number and ErrP bias in a

Monte-Carlo fashion and chose the combination of the

parameters that maximizes the speed.

A drawback of using ErrPs is the fact that one has to

collect a substantial number of error trials in order to

train the ErrP classifier. There are different possible

routes to accomplish this. First, one may perform an

experiment in two successive stages. In the first stage,

spelling would be done without error correction. In the

next stage, the trials would have to be labeled and used

for training an ErrP classifier (just as we did for classifier

B). Second, one could use a calibration phase to collect

trials for the ErrP classifier (as we did for classifier A).

Regarding the second case, our data show that an ErrP

classifier trained in one paradigm (classifier A in the pre-

sent study) can transfer to a similar paradigm, albeit with

a reduced performance. However, the Calibration Speller

is not applicable in a clinical context because it involves

key presses. However, a calibration experiment that

would be completely passive and thus applicable to

patients, similar to the offline calibration phase of the

spelling classifier, could be used to collect ErrP data for

classifier training. It is true that the ErrPs from such a

calibration experiment may have large differences to the

ErrPs obtained in the Center Speller (’observation ErrPs’

instead of ‘interaction ErrPs’). Therefore, the applicability

of such an approach remains to be investigated.

Ultimately, the utility of ErrP detection is dictated by

whether a successful implementation in a clinical setting

is feasible. ErrP detection could be relevant to patients

because their BCI performance is more variable and

often lower than the performance of healthy partici-

pants. However, this critically depends on whether error

potentials can be detected reliably in patients. Regarding

this question, the work of Spüler [29] is instructive. In a

clinical study with four patients, ErrPs were classified

with an accuracy of 71%. Using a Matrix speller, the bit

rate was increased by 35.6% on average. If patients are

in a progressed state of the locked-in syndrome, a possi-

ble approach for calibration of the ErrP classifier would

be to have patients passively observe errors and train on

the resulting observation ErrPs, as outlined above [25].

Conclusion

Concluding, we demonstrated a significant increase of

communication speed of gaze-independent ERP spellers

when error potentials are detected online. Since BCI

performance is often low in patients and successful

detection of ErrPs has been demonstrated in ALS

patients [29], we believe that ErrP detection can comple-

ment conventional BCIs in a clinical application.

Abbreviations

BCI: brain-computer interface; EEG: electroencephalography; MEG:

magnetoencephalography; EOG: electrooculography; ALS: amyotrophic

lateral sclerosis; ERP: event-related potential; ErrP: error-related potential; N

error-negativity; P

: error-positivity; ROC: receiver-operating-characteristic;

AUC: area under the ROC curve; ANOVA: analysis of variance.

Author details

Machine Learning Laboratory, Berlin Institute of Technology, Berlin,

Germany.

Artificial Intelligence Laboratory, Department of Informatics,

University of Zurich, Switzerland, Andreasstrasse 15, 8050 Zurich, Switzerland.

Type−A

lassifiers

Fp1 Fp2

AF7

AF3 AF4

AF8

F5 F3 F1 Fz F2 F4 F6

F10

FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8

T7 C5 C3 C1 Cz C2 C4 C6 T8

TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8

P5 P3 P1 Pz P2 P4 P6

P10

PO7

PO3 POz PO4

PO8

O1 Oz O2

EOGvu AUC

0.5

0.6

0.73

0.8

Type−B Classifiers

Fp1 Fp2

AF7

AF3 AF4

AF8

F5 F3 F1 Fz F2 F4 F6

F10

FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8

T7 C5 C3 C1 Cz C2 C4 C6 T8

TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8

P5 P3 P1 Pz P2 P4 P6

P10

PO7

PO3 POz PO4

PO8

O1 Oz O2

EOGvu

Figure 9 Spatial Classification. Spatial distribution of the AUC

scores for ErrP classifiers of type-A (top) and type-B (bottom) shown

as scalp topography. Classification was performed for each channel

separately. Peak performance was reached at central regions for

both types (A:0.67, B: 0.8), frontal regions are near chance level. The

performance is higher for type-B than for type-A classifiers in all

regions.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 12 of 13

Authors’contributions

NMS, MST and BB conceived and designed the experiments. NMS performed

the experiments and analyzed the data. NMS, MST and BB wrote the paper.

The authors have declared that no competing interests exist. All authors

read and approved the final manuscript.

Received: 14 October 2011 Accepted: 15 February 2012

Published: 15 February 2012

References

1. Kübler A, Müller KR: An introduction to brain computer interfacing. In

Toward Brain-Computer Interfacing. Edited by: Dornhege G, del R Millán J,

Hinterberger T, McFarland D, Müller KR. Cambridge, MA: MIT Press; 2007:1-25.

2. Kübler A, Kotchoubey B, Kaiser J, Wolpaw J, Birbaumer N: Brain-computer

communication: unlocking the locked in. Psychol Bull 2001, 127(3):358-375.

3. Kübler A, Nijboer F, Birbaumer N: Brain-Computer Interfaces for

Communication and Motor Control - Perspectives on Clinical

Applications. In Toward Brain-Computer Interfacing. Edited by: Dornhege G,

de R Millán J, Hinterberger T, McFarland D, Müller KR. Cambridge: MIT Press;

2007:373-391.

4. Huggins JE, Wren PA, Gruis KL: What would brain-computer interface

users want? Opinions and priorities of potential users with amyotrophic

lateral sclerosis. Amyotroph Lateral Scler 2011, 12:318-324, [http://

informahealthcare.com/doi/abs/10.3109/17482968.2011.5 72978].

5. Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, Halder S,

Mochty U, Krusienski DJ, Vaughan TM, Wolpaw JR, Birbaumer N, Kübler A: A

P300-based brain-computer interface for people with amyotrophic

lateral sclerosis. Clin Neurophysiol 2008, 119:1909-1916.

6. Sellers EW, Vaughan TM, Wolpaw JR: A brain-computer interface for long-

term independent home use. Amyotrophic Lateral Sclerosis 2010, 5:449-455,

[http://informahealthcare.com/doi/abs/10.3109/174829610037774 70].

7. McFarland DJ, Krusienski DJ, Sarnacki WA, Wolpaw JR: Emulation of

computer mouse control with a noninvasive brain-computer interface. J

Neural Eng 2008, 5:101-110[http://www.ncbi.nlm.nih.gov/pmc/articles/

PMC2757111].

8. Leeb R, Friedman D, Müller-Putz G, Scherer R, Slater M, Pfurtscheller G: Self-

Paced (Asynchronous) BCI Control of a Wheelchair in Virtual

Environments: A Case Study with a Tetraplegic. Comput Intell Neurosci

2007, 2007:79642.

9. Farwell L, Donchin E: Talking off the top of your head: toward a mental

prosthesis utilizing event-related brain potentials. Electroencephalogr Clin

Neurophysiol 1988, 70:510-523.

10. Lenhardt A, Kaper M, Ritter H: An adaptive P300-based online brain-

computer interface. IEEE Trans Neural Syst Rehabil Eng 2008, 16:121-130.

11. Majaranta P, MacKenzie S, Aula A, Räihä KJ: Effects of feedback and dwell

time on eye typing speed and accuracy. Univ Access Inf Soc 2006,

5(2):199-208.

12. Treder MS, Blankertz B: (C)overt attention and visual speller design in an

ERP-based brain-computer interface. Behav Brain Funct 2010, 6:28[http://

www.behavioralandbrainfunctions.com/content/6/1/28].

13. Brunner P, Joshi S, Briskin S, Wolpaw JR, Bischof H, Schalk G: Does the

“P300”speller depend on eye gaze? J Neural Eng 2010, 7:056013.

14. Acqualagna L, Treder MS, Schreuder M, Blankertz B: A novel brain-

computer interface based on the rapid serial visual presentation

paradigm. Conf Proc IEEE Eng Med Biol Soc 2010, 2010:2686-2689[http://dx.

doi.org/10.1109/IEMBS.2010.5626548].

15. Treder MS, Schmidt NM, Blankertz B: Gaze-independent brain-computer

interfaces based on covert attention and feature attention. J Neural Eng

2011, 8(6):066003[http://dx.doi.org/10.1088/1741-2560/8/6/066003], [Open

Access].

16. Liu Y, Zhou Z, Hu D: Gaze independent brain-computer speller

with covert visual search tasks. Clin Neurophysiol 2011,

122(6):1127-1136.

17. Treder MS, Schmidt NM, Blankertz B: Towards gaze-independent visual

brain-computer interfaces. Front Comput Neurosci 2010 [http://dx.doi.org/

10.3389/conf.fncom.2010.51.00117], [Conference Abstract: Bernstein

Conference on Computational Neuroscience 2010].

18. Schreuder M, Höhne J, Treder MS, Blankertz B, Tangermann M:

Performance Optimization of ERP-Based BCIs Using Dynamic Stopping.

Front Comput Neurosci 2011, 2011:4580-4583.

19. Höhne J, Schreuder M, Blankertz B, Tangermann M: Two-dimensional

auditory P300 Speller with predictive text system. Conf Proc IEEE Eng Med

Biol Soc 2010, 2010:4185-4188[http://dx.doi.org/10.1109/

IEMBS.2010.5627379].

20. Zhang H, Guan C, Wang C: Asynchronous P300-based brain-computer

interfaces: a computational approach with statistical models. IEEE Trans

Biomed Eng 2008, 55:1754-1763.

21. Holroyd C, Coles M: The neural basis of human error processing:

reinforcement learning, dopamine, and the error-related negativity.

Psychol Rev 2002, 109:679-709.

22. Falkenstein M, Hoormann J, Christ S, Hohnsbein J: ERP components on

reaction errors and their functional significance: a tutorial. Biol Psychol

2000, 51(2-3):87-107.

23. Blankertz B, Dornhege G, Schäfer C, Krepki R, Kohlmorgen J, Müller KR,

Kunzmann V, Losch F, Curio G: Boosting bit rates and error detection for

the classification of fast-paced motor commands based on single-trial

eeg analysis. IEEE Trans Neural Syst Rehabil Eng 2003, 11(2):127-131[http://

dx.doi.org/10.1109/TNSRE.2003.814456].

24. Parra L, Spence C, Gerson A, Sajda P: Response error correction - a

demonstration of improved human-machine performance using real-

time eeg monitoring. IEEE Trans Neural Syst Rehabil Eng 2003,

11(2):173-177.

25. Schie HTV, Mars RB, Coles MG, Bekkering H: Modulation of activity in

medial frontal and motor cortices during error observation. Nat Neurosci

2004, 7(5):549-554.

26. Ferrez P, Millán J: You are wrong! - Automatic detection of interaction

errors from brain waves. 19th International Joint Conference on Artificial

Intelligence 2005, 1413-1418.

27. Buttfield A, Ferrez P, Millán JDR: Towards a robust BCI: error

potentials and online learning. IEEE Trans Neural Syst Rehabil Eng

2006, 14:164-168.

28. Ferrez P, Millán J: Error-related eeg potentials generated during

simulated brain-computer interaction. IEEE Trans Biomed Eng 2008,

55:923-929.

29. Spüler M: Online Erkennung von Fehlerpotentialen zur Fehlerkorrektur

bei einem P300 Brain-Computer Interface. Eberhard Karls Universität

Tübingen 2010, [Diploma thesis].

30. Dal Seno B, Matteucci M, Mainardi L: Online Detection of P300 and Error

Potentials in a BCI Speller. Comput Intell Neurosci 2010, 2010:307254[http://

dx.doi.org/10.1155/2010/307254].

31. Combaz A, Chumerin N, Manyakov NV, Robben A, Suykens JAK, Van

Hulle MM: Error-related potential recorded by EEG in the context of a

p300 mind speller brain-computer interface. IEEE International Workshop

on Machine Learning for Signal Processing, IEEE 2010:65-70[http://ieeexplore.

ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5589217].

32. Schalk G, Wolpaw JR, McFarland DJ, Pfurtscheller G: EEG-based

communication: presence of an error potential. Clin Neurophysiol 2000,

111:2138-2144.

33. Llera A, van Gerven M, Gómez V, Jensen O, Kappen H: On the use of

interaction error potentials for adaptive brain computer interfaces.

Neural Netw 2011, 24(10):1120-1127, [http://www.sciencedirect.com/science/

article/pii/S0893608011 001481].

34. Dal Seno B, Matteucci M, Mainardi L: A genetic algorithm for automatic

feature extraction in P300 detection. Proceedings of the International Joint

Conference on Neural Networks (IJCNN’08) 2008, 3145-3152.

35. Ishihara S: Tests for colour-blindness Tokyo: Hongo Harukicho; 1919.

36. Venthur B, Scholler S, Williamson J, Dähne S, Treder MS, Kramarek MT,

Müller KR, Blankertz B: Pyff - a pythonic framework for feedback

applications and stimulus presentation in neuroscience. Front

Neuroscience 2010, 4:179[http://dx.doi.org/10.3389/fnins.2010.00179].

37. Straw AD: Vision egg: an open-source library for realtime visual stimulus

generation. Front Neuroinformatics 2008, 2:4.

38. Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR: Single-trial analysis

and classification of ERP components - a tutorial. NeuroImage 2011,

56:814-825[http://dx.doi.org/10.1016/j.neuroimage.2010.06.048].

doi:10.1186/1471-2202-13-19

Cite this article as: Schmidt et al.: Online detection of error-related

potentials boosts the performance of mental typewriters. BMC

Neuroscience 2012 13:19.

Schmidt et al.BMC Neuroscience 2012, 13:19

http://www.biomedcentral.com/1471-2202/13/19

Page 13 of 13