{'fraction_non_alphanumeric': 0.021052631578947368, 'fraction_numerical': 0.0, 'mean_word_length': 5.891304347826087, '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}}
Comparison of Temporal and Standard Independent Component Analysis (ICA) Algorithms for EEG Analysis

Growing interest in Electroencephalogram (EEG) classification brings a need for the development of appropriate analysis and processing techniques. One of the most significant issues associated with EEG analysis is the high contamination of the recorded signals with various artefacts, both from the subject and from equipment interference. This paper discusses the advantages of using temporal Independent Component Analysis (ICA) over standard ICA for artefact removal from EEG signals. The performance of three ICA algorithms, standard ICA (FastICA) and two extensions including temporal information (Temporal FastICA and TDSEP), has been compared using both artificial and physiological data. It has been found that, in both cases, the temporal algorithm TDSEP displays a significant improvement in performance over the remaining two algorithms.