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Martin del Campo Vera R, Sundaram S, Lee R, Lee Y, Leonor A, Chung RS, Shao A, Cavaleri J, Gilbert ZD, Zhang S, Kammen A, Mason X, Heck C, Liu CY, Kellis S, Lee B. Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus. J Neural Eng 2024; 21:046017. [PMID: 38914073 PMCID: PMC11247508 DOI: 10.1088/1741-2552/ad5b19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/25/2024] [Accepted: 06/24/2024] [Indexed: 06/26/2024]
Abstract
Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.
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Affiliation(s)
- Roberto Martin del Campo Vera
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Shivani Sundaram
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Richard Lee
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Yelim Lee
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Andrea Leonor
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Ryan S Chung
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Arthur Shao
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Jonathon Cavaleri
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Zachary D Gilbert
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Selena Zhang
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Alexandra Kammen
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Xenos Mason
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Christi Heck
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Spencer Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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Phunruangsakao C, Achanccaray D, Izumi SI, Hayashibe M. Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks. Front Hum Neurosci 2022; 16:1032724. [PMID: 36583011 PMCID: PMC9792600 DOI: 10.3389/fnhum.2022.1032724] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
Introduction Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within the same limb remains challenging due to the activation of overlapping brain regions. A single deep learning model may be insufficient to effectively learn discriminative features among tasks. Methods The present study proposes a framework to enhance the decoding of multiple hand-MI tasks from the same limb using a multi-branch convolutional neural network. The CNN framework utilizes feature extractors from established deep learning models, as well as contrastive representation learning, to derive meaningful feature representations for classification. Results The experimental results suggest that the proposed method outperforms several state-of-the-art methods by obtaining a classification accuracy of 62.98% with six MI classes and 76.15 % with four MI classes on the Tohoku University MI-BCI and BCI Competition IV datasets IIa, respectively. Discussion Despite requiring heavy data augmentation and multiple optimization steps, resulting in a relatively long training time, this scheme is still suitable for online use. However, the trade-of between the number of base learners, training time, prediction time, and system performance should be carefully considered.
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Affiliation(s)
- Chatrin Phunruangsakao
- Neuro-Robotics Laboratory, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan,*Correspondence: Chatrin Phunruangsakao
| | - David Achanccaray
- Presence Media Research Group, Hiroshi Ishiguro Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Shin-Ichi Izumi
- Department of Physical Medicine and Rehabilitation, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
| | - Mitsuhiro Hayashibe
- Neuro-Robotics Laboratory, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan,Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan
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Khare SK, Gaikwad N, Bokde ND. An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218128. [PMID: 36365824 PMCID: PMC9657151 DOI: 10.3390/s22218128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/15/2022] [Accepted: 10/21/2022] [Indexed: 06/01/2023]
Abstract
Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.
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Affiliation(s)
- Smith K. Khare
- Department of Electrical & Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
| | - Nikhil Gaikwad
- Department of Electrical & Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
| | - Neeraj Dhanraj Bokde
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
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Datta D, Mallick PK, Bhoi AK, Ijaz MF, Shafi J, Choi J. Hyperspectral Image Classification: Potentials, Challenges, and Future Directions. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3854635. [PMID: 35528334 PMCID: PMC9071975 DOI: 10.1155/2022/3854635] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/22/2022] [Accepted: 03/30/2022] [Indexed: 12/14/2022]
Abstract
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies' contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.
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Affiliation(s)
- Debaleena Datta
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar 751024, India
| | - Pradeep Kumar Mallick
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar 751024, India
| | - Akash Kumar Bhoi
- KIET Group of Institutions, Delhi-NCR, Ghaziabad-201206, India
- Directorate of Research, Sikkim Manipal University, Gangtok 737102, Sikkim, India
- AB-Tech eResearch (ABTeR), Sambalpur, Burla 768018, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Jana Shafi
- Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdul Aziz University, Wadi Ad-Dawasir 11991, Saudi Arabia
| | - Jaeyoung Choi
- School of Computing, Gachon University, Seongnam-si 13120, Republic of Korea
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