Left-handed BCI - examining effects of handedness and hand dominance on EEG grasp classification

Abstract

Brain-Computer Interfaces (BCIs) are of high potential use to individuals whosemotor function is impaired, or who have undergone a loss of limb or limb functionality. Electroencephalography (EEG) is one popular method of collecting signals from the brain, and is commonly used in cases where other sensing methods are difficult or impossible. This method of collecting brain signal data has been shown, when used in conjunction with Electromyography (EMG) data, to be capable of classifying fine hand movements with a high degree of accuracy, providing avenues for the design of highly attenuated prosthetic limbs. Studies which have examined such uses for BCIs, however, seldom examine the effects of handedness, as well as off-hand or dualhanded motion, on classification accuracy. This study examines the effects of hand use and hand dominance on the performance of several classifiers derived from EEG data. Data was collected, using the OpenBCI EEG Electrode Cap Kit, for 16 participants (9 right-handed, 7 left-handed), on a set of 6 grasp types, and a selection of 5 classification algorithms (Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, and Neural Network) commonly found in previous works were used. Outcomes of the study indicate that Neural Networks are best suited among these classifiers to determine hand motion in a dual-handed environment, and that, while providing hand-dominance data for classification training may not improve accuracy in all cases, design and feature changes based on factors such as hand-dominance may improve the performance of BCIs based on EEG data.