Advances in Brain–Computer Interfaces

A special issue of Biomimetics (ISSN 2313-7673).

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 1788

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Guest Editor
Mechanical Engineering, LUT School of Energy Systems, LUT University, Lappeenranta, Finland
Interests: brain–computer interface; rehabilitation; neuro-engineering
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Special Issue Information

Dear Colleagues,

Brain–computer interface (BCI) technology has been introduced to improve the quality of life for people with disabilities or difficulties in their daily lives. BCI applications such as driver assistants, sleep identification for drivers, and controlling a bionic hand/ankle–foot orthosis are widely used for healthy people as well as paralyzed patients. 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 an electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and photoplethysmography (PPG).

The mathematical solutions are signal denoising (filtering), feature extraction, and machine learning algorithms. This collection of articles aims to highlight mathematical innovations as well as novel ideas for designing tasks to induce the brain to generate distinctive neuronal patterns. The final goal of this research topic is the discovery of new methods for 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 determine which method is more beneficial, e.g., wavelet, chaotic methods, common spatial patterns, or reinforcing methods;
  • Developing classifiers to automate identification procedures.

Dr. Amin Hekmatmanesh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biosignal processing
  • pattern recognition
  • machine learning
  • brain–computer interface
  • health monitoring systems

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Published Papers (1 paper)

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Research

18 pages, 1260 KiB  
Article
Brain-Inspired Architecture for Spiking Neural Networks
by Fengzhen Tang, Junhuai Zhang, Chi Zhang and Lianqing Liu
Biomimetics 2024, 9(10), 646; https://doi.org/10.3390/biomimetics9100646 - 21 Oct 2024
Viewed by 1314
Abstract
Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then [...] Read more.
Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces)
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