{'fraction_non_alphanumeric': 0.013874066168623266, 'fraction_numerical': 0.0021344717182497333, 'mean_word_length': 5.513888888888889, '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}}
Towards Decoding 3 D Finger Trajectories from EEG

Brain-Machine interfaces and neural prosthesis use the electrical activity generated by cortical neurons in the brain for controlling external devices such as robotic arms. While many research is based on the invasive recording of the brain electrical activity, very few studies have addressed the possibility of generating the control from non-invasive measurements. In this work we study the 3D decoding of the fingertip motion based on noninvasive EEG signals during self-selected and self-initiated reaching movement task. Several strategies of decoding are studied, temporal or time-frequency information of the EEG signals, position or velocity data of the fingertip motion and the partial least squares regression and support vector regression algorithms. The preliminary results reveal positive correlation between the observed and the reconstructed velocities of the fingertip.