Toward a noninvasive automatic seizure control system in rats with transcranial focal stimulations via tripolar concentric ring electrodes.

Epilepsy affects approximately 1% of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study, we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback.

Design of a robust EMG sensing interface for pattern classification.

Electromyographic (EMG) pattern classification has been widely investigated for neural control of external devices in order to assist with movements of patients with motor deficits. Classification performance deteriorates due to inevitable disturbances to the sensor interface, which significantly challenges the clinical value of this technique. This study aimed to design a sensor fault detection (SFD) module in the sensor interface to provide reliable EMG pattern classification. This module monitored the recorded signals from individual EMG electrodes and performed a self-recovery strategy to recover the classification performance when one or more sensors were disturbed. To evaluate this design, we applied synthetic disturbances to EMG signals collected from leg muscles of able-bodied subjects and a subject with a transfemoral amputation and compared the accuracies for classifying transitions between different locomotion modes with and without the SFD module. The results showed that the SFD module maintained classification performance when one signal was distorted and recovered about 20% of classification accuracy when four signals were distorted simultaneously. The method was simple to implement. Additionally, these outcomes were observed for all subjects, including the leg amputee, which implies the promise of the designed sensor interface for providing a reliable neural-machine interface for artificial legs.