title: EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects.
abstract: The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (approximately 100% in healthy subjects and >80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.
authors.affiliation: Department of Computer Science, Northern Illinois University, USA. jzhou@cs.niu.edu
authors.forename: Jie
authors.initials: J
authors.lastname: Zhou
authors.forename: Jun
authors.initials: J
authors.lastname: Yao
authors.forename: Jie
authors.initials: J
authors.lastname: Deng
authors.forename: Julius P A
authors.initials: JP
authors.lastname: Dewald
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004550:Elbow / Q000294:innervation / Q000502:physiology / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000502:physiology / Q000503:physiopathology; D009068:Movement / Q000502:physiology; D009069:Movement Disorders / Q000209:etiology / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012782:Shoulder / Q000294:innervation / Q000502:physiology / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000150:complications / Q000503:physiopathology*; D019415:Torque