{'fraction_non_alphanumeric': 0.022257551669316374, 'fraction_numerical': 0.00794912559618442, 'mean_word_length': 5.1414634146341465, '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}}
Online motor imagery BCI based on adaptive and incremental linear discriminant analysis algorithm

The traditional Brain-computer Interface (BCI) obtains parameters from the offline analysis and applies them to online experiments. However, due to non-stationary characte-ristic of electroencephalography (EEG), static classification of algorithms are hard to be used in practical BCI. In this paper, we propose a new algorithm that combines the adaptation of preferable new incoming data with the incremental linear discriminant. Then we design a new experiment paradigm and present an adaptive BCI algorithm framework to meet needs of the online experiment. At the end, we look for 8 health subjects to participate in experiments in order to test our algorithm. Results of our experiments showed all subjects could reach 60.7% chance level to the final session and the best result was 65.7% from non-experienced users and 87.9% from people with experiences. These results indicated that our algorithm is effective. In addition, we discuss the difference between subjects and sessions in order to promote accuracy better in future. We consider the presented online experiment method is the first step that towards the fully autocalibrating online BCI system.