Robot- and Brain Computer Interface Therapies for Neurorehabilitation: Current State of the Art and Applications

Dear Colleagues,

Brain–computer Interface (BCI) technology has been introduced to improve the quality of life of people with disabilities or difficulties in their daily lives. BCI applications such as driver assistance, sleep identification for drivers, and controlling bionic hand/ankle–foot orthosis are widely used by healthy people as well as paralyzed patients. BCI studies are not limited exclusively to EEG signals; indeed, other biosignals such as EMG, ECG, and GSR have proven beneficial in BCI applications. Research in the field mainly focuses on the development of mathematical calculations for brain-controlled vehicles, brain-controlled air vehicles, brain-controlled bionic hands, and brain-controlled foot–ankle braces using biosignals collected via electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). Mathematical implementations are divided into five main steps: (1) pre-processing, (2) feature extraction, (3) feature selection, (4) classification, and (5) statistical analysis.

There are also some challenges in the field related to the identification of patterns generated in EEG signals due to motion intention or motion imagination, referred to as event-related synchronization and desynchronization (ERD/ERS). Depending on the BCI tasks, other patterns are generated in EEG signals that are also worthy of attention. These include readiness potentials, steady-state visual evoked potentials, P300s, and generated local evoked potential patterns. Some of the most well-known mathematical formulas and techniques for detecting EEG patterns are wavelets, common spatial patterns, and nonlinear calculations such as chaotic features (entropy, Lyapunov exponent, fractal dimensions, and recurrence graphs). It is also necessary to implement handcrafted features to increase the efficiency of the algorithms. Another challenge is linked to the development of classifiers to automate the procedures, such as support vector machines, deep learning, and neural networks.

BCI and rehabilitation projects have various limitations. In this Special Issue, we intend to focus on mathematical solutions based on signal denoising (filtering), feature extraction, and machine learning algorithms. This collection of articles aims to highlight mathematical innovations as well as new ideas for designing tasks to induce the generation of distinctive neuronal patterns within the brain. Our ultimate goal is to discover novel methods for use in BCI applications. We welcome manuscripts on the following subtopics:

• Decoding brain neuron activities by developing mathematical methods for identifying patterns within the EEG signals automatically
• Identifying EEG patterns relative to human actions and decisions automatically
• Analyzing the patterns generated in a designed task to find out which method is more beneficial, for example, wavelet, chaotic methods, common spatial patterns, or reinforcing methods.

The development of classifiers to automate the identification procedures

Dr. Amin Hekmatmanesh
Guest Editor

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• robot
• brain computer interface
• neurorehabilitation
• machine learning