Zhiqiang Zhang

Bio: Zhiqiang Zhang is an academic researcher from Sheng Jing Hospital. The author has contributed to research in topics: Brain–computer interface & Functional electrical stimulation. The author has an hindex of 1, co-authored 1 publications receiving 47 citations.

SSVEP-Based Brain–Computer Interface Controlled Functional Electrical Stimulation System for Upper Extremity Rehabilitation

Abstract: Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia A brain–computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation The EEG signals based on steady-state visual evoked potential are used in the BCI Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 739% Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia

Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things

Abstract: A brain-computer interface (BCI) for seizure prediction provides a means of controlling epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected but yet can be defined sufficiently to be selectively influenced by strategically implanted electrodes. Challenges remain in offering real-time solutions with such technology because of the immediacy of electrographic ictal behavior. The nonstationary nature of electroencephalographic (EEG) and electrocorticographic (ECoG) signals results in wide variation of both normal and ictal patterns among patients. The use of manually extracted features in a prediction task is impractical and the large amount of data generated even among a limited set of electrode contacts will create significant processing delays. Big data in such circumstances not only must allow for safe storage but provide high computational resources for recognition, capture and real-time processing of the preictal period in order to execute the timely abrogation of the ictal event. By leveraging the potential of cloud computing and deep learning, we develop and deploy BCI seizure prediction and localization from scalp EEG and ECoG big data. First, a new method for epileptic seizure prediction and localization of the seizure focus is presented. Second, an extended optimization approach on existing deep-learning structures, Stacked Auto-encoder and Convolutional Neural Network (CNN), is proposed based on principle component analysis (PCA), independent component analysis (ICA), and Differential Search Algorithm (DSA). Third, a cloud-computing solution (i.e., Internet of Things (IoT)), is developed to define the proposed structures for real-time processing, automatic computing and storage of big data. The ECoG clinical datasets on 11 patients illustrate the superiority of the proposed patient-specific BCI as an alternative to current methodology to offer support for patients with intractable focal epilepsy.

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Abstract: Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

Progress in Brain Computer Interface: Challenges and Opportunities.

Abstract: Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Progress in Brain Computer Interfaces: Challenges and Trends.

Abstract: Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming and artificial intelligence. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and nonlinear brain dynamics and related feature extraction and classification challenges. Psycho-neurophysiological phenomena and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes progress in BCI field and highlights critical challenges.