Automatic Driving Control Method: Latest Advances and Prospects
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".
Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 5063
Special Issue Editors
Interests: brain–computer interface; rehabilitation; neuro-engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The brain–computer interface (BCI) is a technology that has been introduced to improve the quality of life of people with disabilities or difficulties in their daily lives. BCI applications such as driver assistant, sleep identification for drivers, and control of a bionic hand/ankle–foot orthosis are widely used for healthy people as well as paralyzed patients. BCI studies are not limited to EEG signals; indeed, other bio-signals such as EMG, ECG, and GSR are beneficial in BCI applications. BCI has the potential to be used in many applications based on biosignals. 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 from electroencephalograms (EEGs), electrooculograms (EOGs), and electromyograms (EMGs). Mathematical implementations are mainly divided into five main steps: (1) preprocessing, (2) feature extraction, (3) feature selection, (4) classification, and (5) statistical analysis.
Some challenges in the field are related to the identification of patterns generated in EEG signals due to motion intention or motion imagination, called event-related synchronization and desynchronization (ERD/ERS). Depending on BCI tasks, other patterns are generated in EEG signals that deserve attention, such as 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 graph). In addition, it is necessary to use handcrafted features to increase the efficiency of algorithms. Another challenge is linked to the development of classifiers to automate procedures, such as support vector machines, deep learning, and neural networks.
Dr. Amin Hekmatmanesh
Dr. Fan Gao
Guest Editors
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Keywords
- brain–computer interface
- biosignal processing for control of a vehicle
- relahbilitation
