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Biomimetics
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Advances in Brain–Computer Interfaces
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Advances in Brain–Computer Interfaces

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

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 4013

Special Issue Editor

Dr. Amin Hekmatmanesh

E-Mail Website
Guest Editor
Mechanical Engineering, LUT School of Energy Systems, LUT University, Lappeenranta, Finland
Interests: brain–computer interface; rehabilitation; neuro-engineering
Special Issues, Collections and Topics in MDPI journals

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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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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19 pages, 1249 KiB  
Article
Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture
by Chin-Cheng Wu, Cheng-Wei Tsai, Fei-En Wu, Chi-Hsuan Chiang and Jin-Chern Chiou
Biomimetics 2025, 10(2), 79; https://doi.org/10.3390/biomimetics10020079 - 26 Jan 2025
Viewed by 321
Abstract
Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for [...] Read more.
Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition. The model extracted gait features using pressure-sensitive mats, focusing on foot pressure distribution and foot size during walking. Preprocessing techniques, including region of interest (ROI) selection, feature image extraction, and data horizontal flipping, were utilized to establish a CNN model that assessed gait recognition accuracy under two conditions: without carried items and carrying a 500 g object. To extend the application of the CNN to open set recognition for unauthorized personnel, the proposed convolutional neural network-autoencoder (CNN-AE) architecture compressed the average foot pressure map into a 64-dimensional feature vector and facilitated identity determination based on the distances between these vectors. Among 60 participants, 48 were classified as authorized individuals and 12 as unauthorized. Under the condition of not carrying an object, an accuracy of 91.218%, precision of 93.676%, recall of 90.369%, and an F1-Score of 91.993% were achieved, indicating that the model successfully identified most actual positives. However, when carrying a 500 g object, the accuracy was 85.648%, precision was 94.459%, recall was 84.423%, and the F1-Score was 89.603%. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces)
24 pages, 9053 KiB  
Article
An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP
by Behzad Yousefipour, Vahid Rajabpour, Hamidreza Abdoljabbari, Sobhan Sheykhivand and Sebelan Danishvar
Biomimetics 2024, 9(12), 761; https://doi.org/10.3390/biomimetics9120761 - 14 Dec 2024
Viewed by 1133
Abstract
In recent years, significant advancements have been made in the field of brain–computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial–temporal characteristics of EEG signals, which are [...] Read more.
In recent years, significant advancements have been made in the field of brain–computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial–temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces)
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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 1906
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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