Predicting Object-Mediated Gestures From Brain Activity: An EEG Study on Gender Differences

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 3; pp. 411 - 418
• Main Authors: Catrambone, Vincenzo, Greco, Alberto, Averta, Giuseppe, Bianchi, Matteo, Valenza, Gaetano, Scilingo, Enzo Pasquale
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Body mass; Brain; Brain - physiology; Brain Mapping; Brain-Computer Interfaces; EEG; Electroencephalography; Electromyography; Exploitation; Female; Frequency dependence; Functional magnetic resonance imaging; Gender aspects; gender difference; Gender differences; gesture prediction; Gestures; Healthy Volunteers; Humans; Magnetic Resonance Imaging; Male; Microsoft Windows; Motor planning; Movement; Neuroimaging; Parietal Lobe - physiology; pattern recognition; Planning; Predictions; Prefrontal Cortex - physiology; Protocols; Reproducibility of Results; Sex Characteristics; Sex differences; Task analysis; Upper Extremity; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2898469

Recent functional magnetic resonance imaging (fMRI) studies have identified specific neural patterns related to three different categories of movements: intransitive (i.e., meaningful gestures that do not include the use of objects), transitive (i.e., actions involving an object), and tool-mediated (i.e., actions involving a tool to interact with an object). However, fMRI intrinsically limits the exploitation of these results in a real scenario, such as a brain-machine interface. In this paper, we propose a new approach to automatically predict intransitive, transitive, or tool-mediated movements of the upper limb using electroencephalography (EEG) spectra estimated during a motor planning phase. To this end, high-resolution EEG data gathered from 33 healthy subjects were used as input of a three-class k-nearest neighbors classifier. Different combinations of EEG-derived spatial and frequency information were investigated to find the most accurate feature vector. In addition, we studied gender differences further splitting the dataset into only-male data, and only-female data. A remarkable difference was found between accuracies achieved with male and female data, the latter yielding the best performance (78.55% of accuracy for the prediction of intransitive, transitive, and tool-mediated actions). These results potentially suggest that different gender-based models should be employed for the future BMI applications.