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Akila V, Christaline JA, Edward AS. Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning. Diagnostics (Basel) 2024; 14:1008. [PMID: 38786306 PMCID: PMC11119315 DOI: 10.3390/diagnostics14101008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.
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Affiliation(s)
- V. Akila
- Department of ECE, SRM Institute of Science and Technology, Vadapalani, Chennai 600026, India; (J.A.C.); (A.S.E.)
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Pope KJ, Lewis TW, Fitzgibbon SP, Janani AS, Grummett TS, Williams PAH, Battersby M, Bastiampillai T, Whitham EM, Willoughby JO. Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders. Brain Behav 2022; 12:e2721. [PMID: 35919931 PMCID: PMC9480942 DOI: 10.1002/brb3.2721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/05/2022] [Accepted: 07/07/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilized to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognized. Here, we seek to emphasize the extent of residual EMG contamination of EEG. METHODS We compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully paralyzed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the six subjects, to the power of unpruned recordings in the same subjects when paralyzed. We produced "contamination graphs" for different pruning methods. RESULTS EMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz. CONCLUSION Given probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to reanalyze recorded data, reevaluate conclusions from high-frequency EEG data, and be aware of limitations of the methods.
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Affiliation(s)
- Kenneth J Pope
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Medical Device Research Institute, Flinders University, Adelaide, South Australia, Australia
| | - Trent W Lewis
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Medical Device Research Institute, Flinders University, Adelaide, South Australia, Australia
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Azin S Janani
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Tyler S Grummett
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Medical Device Research Institute, Flinders University, Adelaide, South Australia, Australia.,Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Patricia A H Williams
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Flinders Digital Health Research Centre, Flinders University, Adelaide, South Australia, Australia
| | - Malcolm Battersby
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Psychiatry, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - Tarun Bastiampillai
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Psychiatry, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - Emma M Whitham
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Neurology, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - John O Willoughby
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Neurology, Flinders Medical Centre, Adelaide, South Australia, Australia
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Frolov A, Bobrov P, Biryukova E, Isaev M, Kerechanin Y, Bobrov D, Lekin A. Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain-Computer Interface Experiments. Front Robot AI 2021; 7:88. [PMID: 33501255 PMCID: PMC7805631 DOI: 10.3389/frobt.2020.00088] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 06/02/2020] [Indexed: 11/21/2022] Open
Abstract
In this study, the sources of EEG activity in motor imagery brain–computer interface (BCI) control experiments were investigated. Sixteen linear decomposition methods for EEG source separation were compared according to different criteria. The criteria were mutual information reduction between the source activities and physiological plausibility. The latter was tested by estimating the dipolarity of the source topographic maps, i.e., the accuracy of approximating the map by potential distribution from a single current dipole, as well as by the specificity of the source activity for different motor imagery tasks. The decomposition methods were also compared according to the number of shared components found. The results indicate that most of the dipolar components are found by the Independent Component Analysis Methods AMICA and PWCICA, which also provided the highest information reduction. These two methods also found the most task-specific EEG patterns of the blind source separation algorithms used. They are outperformed only by non-blind Common Spatial Pattern methods in terms of pattern specificity. The components found by all of the methods were clustered using the Attractor Neural Network with Increasing Activity. The results of the cluster analysis revealed the most frequent patterns of electrical activity occurring in the experiments. The patterns reflect blinking, eye movements, sensorimotor rhythm suppression during the motor imagery, and activations in the precuneus, supplementary motor area, and premotor areas of both hemispheres. Overall, multi-method decomposition with subsequent clustering and task-specificity estimation is a viable and informative procedure for processing the recordings of electrophysiological experiments.
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Affiliation(s)
- Alexander Frolov
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia.,Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
| | - Pavel Bobrov
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia.,Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
| | - Elena Biryukova
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia.,Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
| | - Mikhail Isaev
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia.,Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
| | - Yaroslav Kerechanin
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia.,Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
| | - Dmitry Bobrov
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Alexander Lekin
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia
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