Brain computer interface analysis using wavelet transforms and auto regressive coefficients

The idea of an EEG based BCI is to assist the people unable to communicate their thoughts due to neuromuscular disorders and hence affected by motor disabilities. The BCI helps them acting as an interface between the human mind and the computer. In this paper an offline analysis of the EEG data recorded from the C3 and C4 electrodes pertaining to motor activities was done. The data obtained was preprocessed with techniques like wavelet transform and linear predictive coding was applied to it to determine the auto regressive coefficients which are treated as feature vectors to train an artificial neural network for appropriate classification. The trained net was then subjected to testing of data from 140 random trials that were taken and the accuracy was determined. The efficiency of this approach was found to be 71.5%

Therapist-mediated post-stroke rehabilitation using haptic/graphic error augmentation

Recent research has suggested that enhanced retraining for stroke patients using haptics (robotic forces) and graphics (visual display) to generate a practice environment that can artificially enhance error rather than reducing it, can stimulate new learning and foster accelerated recovery. We present an evaluation of early results of this novel post-stroke robotic-aided therapy trial that incorporates these ideas in a large VR system and simultaneously employs the patient, the therapist, and the technology to accomplish effective therapy

Arm control recovery enhanced by error augmentation

Here we present results where nineteen stroke survivors with chronic hemiparesis simultaneously employed the trio of patient, therapist, and machine. Massed practice combined with error augmentation, where haptic (robotic forces) and graphic (visual display) distortions are used to enhance the feedback of error, was compared to massed practice alone. The 6-week randomized crossover design involved approximately 60 minutes of daily treatment three times per week for two weeks, followed by one week of rest, and then repeated using the alternate treatment protocol. A therapist provided a visual target using a tracking device that moved a cursor in front of the patient, who was instructed to maintain the cursor on the target. The patient, therapist, technician-operator, and rater were blinded to treatment type. Several clinical measures gauged outcomes at the beginning and end of each 2-week period and one week post training. Results showed incremental benefit across most but not all days, abrupt gains in performance, and a benefit to error augmentation training in final evaluations. This application of interactive technology may be a compelling new method for enhancing a therapist's productivity in stroke-rehabilitation