Bioinspiration: The Path from Engineering to Nature II

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2873

Special Issue Editors


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Guest Editor
LIANA Lab (IA Lab for Natural Sciences), Department of Mechatronics Engineering, Tecnológico de Costa Rica, Cartago 30101, Costa Rica
Interests: artificial neural networks; evolutionary computation; AI-assisted design and modelling
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Guest Editor
Escuela de Ingeniería Eléctrica, Universidad de Costa Rica, San José 11501-2060, Costa Rica
Interests: computer science; artificial intelligence; intelligent systems; speech synthesis; speech recognition; electrical engineering

Special Issue Information

Dear Colleagues,

Bioinspiration, understood as the use of biological processes for inspiration in engineering and computational designs, has become a widespread approach for both the engineer and the computational scientist to study, model, and resolve complex issues. Technological advances, such as the increasing affordability of high-performance computational resources, extensive, fast, and accessible storage capacities, high-speed communication networks, along with a growing and vibrant international practitioner community, make the field an attractive opportunity to develop innovative solutions to the increasingly complex and uncertain issues humanity is facing today, which are impossible to face by means of classical or analytical paradigms.

Furthermore, it has also been recognized that bioinspired approaches stimulate synergy among scientific disciplines. Multi- and transdisciplinary work have become essential for the advancement of this area. Researchers from different knowledge fields can contribute toward unified goals, sharing their perspectives through discussion, interaction, and collaboration, which leads to bioinspired knowledge discovery and dissemination. Such processes enrich scientists’ respective areas of interest and open new possibilities for their studies.

It is with this horizon in mind that we have launched this Special Issue. Published works are expected to be the result of multi- and transdisciplinary efforts, which present innovative findings beyond each expert’s specific knowledge area. We look forward to contributions that not only propose new bioinspired engineering and computational methods and solutions, but are exemplary of an effective and rewarding collaboration between colleagues from diverse areas that will continue to contribute to the growth of the field.

Dr. Juan Luis Crespo-Mariño
Dr. Marvin Coto-Jiménez
Guest Editors

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Keywords

  • biodiversity informatics (application of ICT to biodiversity conservation)
  • bioinformatics and biocomputing
  • bioinspired artificial intelligence
  • bioinspired big data analysis
  • bioinspired electronic and computational devices
  • bioinspired high-performance computing
  • bioinspired image and sound processing for health and life sciences
  • bioinspired machine learning
  • bioinspired materials and biomimetics
  • bioinspired robotics
  • bioinspired social network analysis and modeling
  • bioinspired pattern recognition and classification
  • bioinspired signal recognition 
  • computational systems biology
  • computer-related genomics and molecular structure, function, and evolution
  • computer-related issues regarding the structure and function of DNA and RNA, and the interaction of proteins
  • dynamic models of metabolic, signaling, and gene expression networks
  • evolutionary systems
  • signal and image detection, acquisition, analysis, and processing
  • social network analysis and modeling
  • pattern recognition for biological and related signals
  • bioinformatics, biocomputing, and computational system biology
  • data mining and machine learning
  • healthcare informatics
  • robotics
  • biomedical devices
  • machine learning in agriculture
  • biodiversity informatics
  • visual analytics for biological information
  • high performance computing for health and life sciences
  • models of biological learning
  • brain–machine interfaces

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Published Papers (1 paper)

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Research

20 pages, 1008 KiB  
Article
EEG-Based Classification of Spoken Words Using Machine Learning Approaches
by Denise Alonso-Vázquez, Omar Mendoza-Montoya, Ricardo Caraza, Hector R. Martinez and Javier M. Antelis
Computation 2023, 11(11), 225; https://doi.org/10.3390/computation11110225 - 10 Nov 2023
Cited by 1 | Viewed by 2384
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects the nerve cells in the brain and spinal cord. This condition leads to the loss of motor skills and, in many cases, the inability to speak. Decoding spoken words from electroencephalography (EEG) signals [...] Read more.
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects the nerve cells in the brain and spinal cord. This condition leads to the loss of motor skills and, in many cases, the inability to speak. Decoding spoken words from electroencephalography (EEG) signals emerges as an essential tool to enhance the quality of life for these patients. This study compares two classification techniques: (1) the extraction of spectral power features across various frequency bands combined with support vector machines (PSD + SVM) and (2) EEGNet, a convolutional neural network specifically designed for EEG-based brain–computer interfaces. An EEG dataset was acquired from 32 electrodes in 28 healthy participants pronouncing five words in Spanish. Average accuracy rates of 91.04 ± 5.82% for Attention vs. Pronunciation, 73.91 ± 10.04% for Short words vs. Long words, 81.23 ± 10.47% for Word vs. Word, and 54.87 ± 14.51% in the multiclass scenario (All words) were achieved. EEGNet outperformed the PSD + SVM method in three of the four classification scenarios. These findings demonstrate the potential of EEGNet for decoding words from EEG signals, laying the groundwork for future research in ALS patients using non-invasive methods. Full article
(This article belongs to the Special Issue Bioinspiration: The Path from Engineering to Nature II)
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