A novel multiclass-based framework for P300 detection in BCI matrix speller: Temporal EEG patterns of non-target trials vary based on their position to previous target stimuli

Brain-computer interface (BCI) provides a new communication pathway for severely disabled people and enables them to communicate with external world using only their brain activity. P300-based BCI speller helps patients spell words using their brain signals. Until now, binary-classification-based approaches have been used for P300 detection. This study demonstrates that binary-classification-based approaches may not be appropriate for P300 detection. We proved that temporal EEG patterns of non-target trials are different based on their position to previous target stimuli. Therefore, considering all non-target trials in only one group makes distinguishing target from non-target components difficult for machine learning algorithms and, consequently, deteriorate character recognition accuracy. This study introduced a novel approach for P300 detection in BCI spellers. In this study, we first divided non-target trials into several groups according to their temporal patterns in training stage. Then, we introduced a multiclass-based framework for P300 detection. Proposed approach is evaluated with three public datasets, BCI competition II, BCI competition III and the BNCI Horizon. In all three datasets, proposed multi-class approach outperformed common binary-classification-based approach in the same conditions. In addition, multiclass-based approach reached the highest accuracy (100%) in the 3th sequence for BCI competition II, for BCI competition III achieved an average accuracy of 74% and 98% in 5th and 15th sequences and, for the BNCI Horizon dataset achieved an average accuracy of 77.50% and 97.49% in 5th and 10th sequences, respectively. That means our proposed approach, regardless of its simplicity, achieved state-of-the-art character recognition performance in the existing methods. The results confirmed that binary-classification based methods is not appropriate for P300 detection in BCI spellers.