Table_1_A toolbox for decoding BCI commands based on event-related potentials.XLSX

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.</p

Table_1_A toolbox for decoding BCI commands based on event-related potentials.XLSX

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.</p

Neuropsychological test schedule.

<p>The schedule consists of two blocks of tasks which involve incidental (block 1) or intentional (block 2) learning and retrieval of verbal material. Each block is preceded by a rest phase of 5 minutes duration. Learning tasks ( and , 3 minutes duration) are followed by a figural fluency task (FF, 1 minutes duration) and a rest phase of 3 minutes duration. Retrieval tasks ( and , 3 minutes duration) consist of a free recall of words listened to/memorized during the respective learning task.</p

Exemplary temporal evolutions of global statistical network characteristics during different neuropsychological tasks.

<p>Time courses of average shortest path length (left) and clustering coefficient (right) from an epilepsy patient (red lines) and a control subject (black lines). Baseline recording (); incidental learning task (); intentional learning task (). Lines are for eye-guidance only.</p

Comparison of the distributions of task-induced modifications of global statistical network characteristics.

<p>Box-plots (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080273#pone-0080273-g003" target="_blank">Figure 3</a> for details) of the relative deviation of the average shortest path length (left) and of the clustering coefficient (right) during the learning tasks ( and ) from the respective values during the baseline recording .</p

Relationships between task-induced modifications of global statistical network characteristics and recall performances.

<p>Scatterplots of relative deviations of the average shortest path length (top) and of the clustering coefficient (bottom) during incidental (left) and intentional learning (right) from the respective values during the baseline recording and subsequent recall performances and . A significant correlation could only be observed between the relative clustering coefficient and the number of recalled words during (linear regression is represented with a solid black line).</p

Comparison of the distributions of global statistical network characteristics from 33 subjects for different neuropsychological tasks.

<p>Box-plots of the average shortest path length (left) and the clustering coefficient (right) for the baseline recording () and for the incidental () and the intentional learning task (). Bottom and top of a box are the first and third quartiles, and the band inside a box is the median. The ends of the whiskers represent the minimum and maximum of the data.</p

Calculation of the trough to peak ratio (TPR).

<p>We quantified paCFC as the ratio between trough (local minima of the time series - red vertical lines) and HG amplitude at the corresponding trough. Around each detected trough we spanned a window (half cycle - gray bars) in which activity (black bold line) and HG amplitude (green line) was averaged.</p

Behavioral data.

<p>For SRT (serial reaction time task) and GNG (Go/No-Go) task reaction time is shown (standard deviation) in msec. For AMCT (auditory-motor coordination task) the absolute deviation from precision is shown also in msec. Each trialbin encompasses 30 trials.</p

Paradigms employed (details described in Methods).

<p>A) Serial reaction time task: The numbers on the screen indicate the finger to be used for the key press. B) Go/no-go: Green indicates a go and red indicates a no-go trial. C) Auditory motor coordination: Subjects were instructed to press a key in the middle of the interval between two consecutive tones. The interval length was either one second or two seconds and was held fixed for one minute. Each subject carried out two blocks (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089576#s4" target="_blank">Methods</a>).</p