Applications of Brain–Machine Interfaces
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 254
Special Issue Editors
Interests: brain–machine interfaces; human–machine collaborative systems; haptic feedback control systems; computer vision; control systems
Special Issue Information
Dear Colleagues,
As a direct communication channel between the brain and external devices, the brain–machine interface (BMI) receives wide attention. With the development of techniques for brain signal acquisition and processing, the BMI is becoming cheaper and more commonly considered in various fields, ranging from assistive environments to medical treatment and military purposes.
Among various techniques contributing to BMI development, AI has huge potential to boost the performance of BMI applications. However, there are still plenty challenges in BMI applications leading to low reliability, low classification rates (or intention detection rates), and the inter-session and inter-subject varieties, which sometimes make the BMI performance differ from subject to subject. The literature shows that quite a few factors can negatively affect the performance of BMI applications, including hardware, software, algorithms, individual user characteristics, and so on. The human factors have a significant negative influence on the low intention detection rate, for instance, the concentration level, time-varying mental behavior, subject varieties, and so on.
This Special Issue focuses on Applications of Brain–Machine Interfaces, for which both overview and innovative articles are welcome for submission. Topics of interest include, but are not limited to, the following topics:
- The state-of-the-art approaches for brain signal acquisition and processing for BMI applications.
The latest development of brain signal acquisition, brain behavior detection, classification, and identification techniques and the potential to be applied in BMI.
- The challenges of BMI applications.
Challenges and difficulties of BMI when employed in real-world applications. Approaches and potential to overcome these challenges and difficulties.
- Invasive and non-invasive BMI applications.
- Applications of BMI in medical, psychology, assistive techniques, training, and other fields.
- Integrating AI in BMI applications.
- BMI applications for human–machine collaborative systems.
Dr. Shengzhi Du
Prof. Dr. Qingshan She
Guest Editors
Manuscript Submission Information
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Keywords
- brain signal acquisition
- brain signal processing
- brain–machine interfaces
- human–machine systems
