{'fraction_non_alphanumeric': 0.02279874213836478, 'fraction_numerical': 0.0023584905660377358, 'mean_word_length': 5.461928934010152, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Recognition of SSMVEP signals based on multi-channel integrated GT2circ statistic method

Brain-computer interface (BCI) is a modern useful tool of bypassing usual channels of muscle and peripheral nervous system to establish a direct connection between brain and external devices and to restore fundamental communication and control skills. Steady-state visual evoked potential (SSVEP), as one of the most popular EEG modality, has been widely used in BCI applications. For SSVEP BCI, the most challenging task is to effectively improve the accuracy, especially in minimum number of recording electrodes and short stimulation duration. In this study, a novel multi-channel integrated GT2circ statistic method was proposed for the frequency recognition in a four-class steady-state motion visual evoked potential (SSMVEP)-based BCI. The proposed method was compared with the widely used canonical correlation analysis (CCA) and verified with three-channel EEG data from three healthy subjects. Results indicated that a higher recognition performance with shorter recording time and few electrodes can be achieved by using of this novel method rather than CCA method, making multi-channel integrated GT2circ statistic a robust approach for the implementation of SSVEP BCIs.