Publication_Volosyak

DOI: 10.3390/brainsci12020234

Abstract

This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain–computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user’s own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session’s data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.

DOI: 10.3389/fncom.2022.939202

DOI: 10.1007/978-3-030-85099-9_6

Abstract

This paper investigates the effects of the training process on the classification accuracy for a steady-state motion visual evoked potentials (SSMVEP)-based brain-computer interface (BCI) paradigm. An SSMVEP-based BCI works similar to SSVEP with the main difference that the stimulus is smoothly changing its appearance, with a continuous motion, leading to less user fatigue. Typical SSMVEP classification utilises correlation algorithms to compare the incoming Electroencephalography (EEG) data with a sine-cosine template. To increase the classification performance of BCI algorithms, collecting user training data has been a common practice recently, usually for template-matching detection algorithms. The incoming EEG data are compared with an individually created template from the user’s own pre-recorded EEG response to the stimulus. In this offline study, previously recorded data (3 s of training EEG data), which were collected during an online experiment with 86 participants, were used. Task-related component analysis (TRCA), a state of the art classification method, was modified with the spatial filter W generated by the canonical-correlation analysis (CCA). The TRCA and the sine+cosine templates were compared. The cross paradigm utilisation of the training data was also investigated, e.g. the TRCA model built from SSVEP training data was used to classify the SSMVEP data and vice versa. Results show a significant difference in favour of the usage of the training data over the sine-cosine template for the SSMVEP paradigm classification. A cross-paradigm validation shows promising results (accuracy >70%) for a time window of 1.5 s, similar to the sine+cosine templates. The trained TRCA models achieved an accuracy of 94% and 97% for the SSMVEP and SSVEP paradigms, respectively, in the mentioned time window. Overall, we conclude that training can significantly improve the SSMVEP target classification.

DOI: https://doi.org/10.1088/2057-1976/ab87e6

DOI: https://doi.org/10.1155/2020/7985010.

DOI: https://doi.org/10.1109/THMS.2020.2983661.

DOI: https://doi.org/10.3390/brainsci10040240.