Online Detection of P300 and Error Potentials in a BCI Speller

Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances

The cost of space independence in P300-BCI spellers.

Background: Though non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user. Methods: In healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller. Results: EEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller’s performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed. Conclusions: The advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus

Evaluating true BCI communication rate through mutual information and language models.

Brain-computer interface (BCI) systems are a promising means for restoring communication to patients suffering from "locked-in" syndrome. Research to improve system performance primarily focuses on means to overcome the low signal to noise ratio of electroencephalogric (EEG) recordings. However, the literature and methods are difficult to compare due to the array of evaluation metrics and assumptions underlying them, including that: 1) all characters are equally probable, 2) character selection is memoryless, and 3) errors occur completely at random. The standardization of evaluation metrics that more accurately reflect the amount of information contained in BCI language output is critical to make progress. We present a mutual information-based metric that incorporates prior information and a model of systematic errors. The parameters of a system used in one study were re-optimized, showing that the metric used in optimization significantly affects the parameter values chosen and the resulting system performance. The results of 11 BCI communication studies were then evaluated using different metrics, including those previously used in BCI literature and the newly advocated metric. Six studies' results varied based on the metric used for evaluation and the proposed metric produced results that differed from those originally published in two of the studies. Standardizing metrics to accurately reflect the rate of information transmission is critical to properly evaluate and compare BCI communication systems and advance the field in an unbiased manner

Online detection of error-related potentials boosts the performance of mental typewriters

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>In our study with eleven participants, an ErrP detection mechanism was implemented in an electroencephalography (EEG) based gaze-independent visual speller.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)–(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI

Preliminary results using a P300 brain-computer interface speller: a possible interaction effect between presentation paradigm and set of stimuli

Fernández-Rodríguez Á., Medina-Juliá M.T., Velasco-Álvarez F., Ron-Angevin R. (2019) Preliminary Results Using a P300 Brain-Computer Interface Speller: A Possible Interaction Effect Between Presentation Paradigm and Set of Stimuli. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11506. Springer, ChamSeveral proposals to improve the performance controlling a P300-based BCI speller have been studied using the standard row-column presentation (RCP) par-adigm. However, this paradigm could not be suitable for those patients with lack of gaze control. To solve that, the rapid serial visual presentation (RSVP) para-digm, which presents the stimuli located in the same position, has been proposed in previous studies. Thus, the aim of the present work is to assess if a stimuli set of pictures that improves the performance in RCP, could also improve the per-formance in a RSVP paradigm. Six participants have controlled four conditions in a calibration task: letters in RCP, pictures in RCP, letters in RSVP and pictures in RSVP. The results showed that pictures in RCP obtained the best accuracy and information transfer rate. The improvement effect given by pictures was greater in the RCP paradigm than in RSVP. Therefore, the improvements reached under RCP may not be directly transferred to the RSVP.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

P300, Steady State Visual Evoked Potentials, And Hybrid Paradigms For A Brain Computer Interface Speller

The goal of this research was to evaluate and compare two types of brain computer interface (BCI) systems, P300 and steady state visually evoked potentials (SSVEP), as spelling paradigms and combine them as a hybrid approach. There were pilot experiments performed for designing the parameters of the SSVEP spelling paradigm including peak detection for different range of frequencies, placement of LEDs, design of the SSVEP stimulus board, and window time for the SSVEP peak detection processing. The next experiment was to evaluate the SSVEP spelling paradigm. Six subjects participated in the task. The accuracy of each frequency and average accuracy for each subject were considered. The second experiment was designed to compare the performance and accuracy of SSVEP, P300, and the combination of both paradigms as a simultaneous task. Ten subjects were considered for performing this experiment. Overall the average accuracy of the SSVEP spelling paradigm was 80.00 % and higher than the P300 spelling paradigm average accuracy which was 72.50 %, and both of the spelling paradigms have better accuracy than the hybrid paradigm with the average accuracy of 64.39 %