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Li X, Wang D, Zhang B, Fan C, Chen J, Xu M, Chen Y. [A review on electroencephalogram based channel selection]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2024; 41:398-405. [PMID: 38686423 PMCID: PMC11058489 DOI: 10.7507/1001-5515.202308034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.
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Affiliation(s)
- Xiangzhe Li
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Dan Wang
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Baiwen Zhang
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing 100089, P.R. China
| | - Chaojie Fan
- School of Transportation Engineering, Central South University, Changsha 410075, P.R. China
| | - Jiaming Chen
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Meng Xu
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Yuanfang Chen
- Beijing Institute of Mechanical Equipment, Beijing 100854, P.R. China
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Liang W, Jin J, Daly I, Sun H, Wang X, Cichocki A. Novel channel selection model based on graph convolutional network for motor imagery. Cogn Neurodyn 2023; 17:1283-1296. [PMID: 37786654 PMCID: PMC10542066 DOI: 10.1007/s11571-022-09892-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/03/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.
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Affiliation(s)
- Wei Liang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
- Shenzhen Research Institute of East China University of Technology, Shenzhen, 518063 China
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology, Moscow, Russia 143026
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
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Zeng Z, Tao L, Zhu H, Zhu Y, Meng L, Fan J, Chen C, Chen W. A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements. IEEE J Transl Eng Health Med 2023; 12:56-65. [PMID: 38088999 PMCID: PMC10712680 DOI: 10.1109/jtehm.2023.3320713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 07/16/2023] [Accepted: 09/21/2023] [Indexed: 12/18/2023]
Abstract
Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson's disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. OBJECTIVE In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. METHODS AND PROCEDURES To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. RESULTS Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from -50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. CONCLUSION We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson's disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.
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Affiliation(s)
- Zheng Zeng
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
| | - Linkai Tao
- Department of Industrial DesignEindhoven University of Technology5600 MBEindhovenThe Netherlands
| | - Hangyu Zhu
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
| | - Yunfeng Zhu
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
| | - Long Meng
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
| | - Jiahao Fan
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
| | - Chen Chen
- Human Phenome Institute, Fudan UniversityShanghai201203China
| | - Wei Chen
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
- Human Phenome Institute, Fudan UniversityShanghai201203China
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潘 礼, 丁 忆, 王 顺, 宋 爱. [Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39:1173-1180. [PMID: 36575087 PMCID: PMC9927178 DOI: 10.7507/1001-5515.202112023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 09/26/2022] [Indexed: 12/29/2022]
Abstract
Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
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Affiliation(s)
- 礼正 潘
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
- 东南大学 远程测控技术江苏省重点实验室(南京 210096)Provincial Key Laboratory of Remote Measurement and Control Technology, Southeast University, Nanjing 210096, P.R. China
| | - 忆 丁
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 顺超 王
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 爱国 宋
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
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Gannouni S, Aledaily A, Belwafi K, Aboalsamh H. Electroencephalography based emotion detection using ensemble classification and asymmetric brain activity. J Affect Disord 2022; 319:416-27. [PMID: 36162677 DOI: 10.1016/j.jad.2022.09.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/07/2022] [Accepted: 09/20/2022] [Indexed: 11/22/2022]
Abstract
Over the past decade, emotion detection using rhythmic brain activity has become a critical area of research. The asymmetrical brain activity has garnered the most significant level of research attention due to its implications for the study of emotions, including hemispheric asymmetry or, more generally, asymmetrical brain activity. This study aimed at enhancing the accuracy of emotion detection using Electroencephalography (EEG) brain signals. This happens by identifying electrodes where relevant brain activity changes occur during the emotions and by defining pairs of relevant electrodes having asymmetric brain activities during emotions. Experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. These results were improved by processing not the whole EEG signals but by focusing on fragments of the signals, called epochs, which represent the instants where the excitation is maximum during emotions. The epochs were extracted using the zero-time windowing method and the numerator group-delay function.
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Guttmann-Flury E, Sheng X, Zhu X. Channel selection from source localization: A review of four EEG-based brain-computer interfaces paradigms. Behav Res Methods 2022. [PMID: 35794417 DOI: 10.3758/s13428-022-01897-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2022] [Indexed: 11/08/2022]
Abstract
Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.
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Zhang X, Meng QH, Zeng M. A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds. J Neural Eng 2022; 19. [PMID: 35732136 DOI: 10.1088/1741-2552/ac7b4a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/22/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels. APPROACH In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry (RG) classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search (BHS) algorithm, including an opposition-based learning strategy (OBL) for generating high-quality initial population, an adaptive parameter strategy (APS) for improving search capability, and a bitwise operation strategy (BOS) for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels. MAIN RESULTS With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy. SIGNIFICANCE The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.
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Affiliation(s)
- Xiaonei Zhang
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
| | - Qing-Hao Meng
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
| | - Ming Zeng
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
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Kim M, Yoo S, Kim C. Miniaturization for wearable EEG systems: recording hardware and data processing. Biomed Eng Lett 2022; 12:239-50. [PMID: 35692891 DOI: 10.1007/s13534-022-00232-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 12/05/2022] Open
Abstract
As more people desire at-home diagnosis and treatment for their health improvement, healthcare devices have become more wearable, comfortable, and easy to use. In that sense, the miniaturization of electroencephalography (EEG) systems is a major challenge for developing daily-life healthcare devices. Recently, because of the intertwined relationship between EEG recording and processing, co-research of EEG recording hardware and data processing has been emphasized for whole-in-one miniaturized EEG systems. This paper introduces miniaturization techniques in analog-front-end hardware and processing algorithms for such EEG systems. To miniaturize EEG recording hardware, various types of compact electrodes and mm-sized integrated circuits (IC) techniques including artifact rejection are studied to record accurate EEG signals in a much smaller manner. Active electrode and in-ear EEG technologies are also researched to make small-form-factor EEG measurement structures. Furthermore, miniaturization techniques for EEG processing are discussed including channel selection techniques that reduce the number of required electrode channel and hardware implementation of processing algorithms that simplify the EEG processing stage.
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Al-Qazzaz NK, Sabir MK, Al-Timemy AH, Grammer K. An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs. Med Biol Eng Comput 2022; 60:531-50. [PMID: 35023073 DOI: 10.1007/s11517-021-02452-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 10/01/2021] [Indexed: 12/15/2022]
Abstract
Investigating gender differences based on emotional changes using electroencephalogram (EEG) is essential to understand various human behavior in the individual situation in our daily life. However, gender differences based on EEG and emotional states are not thoroughly investigated. The main novelty of this paper is twofold. First, it aims to propose an automated gender recognition system through the investigation of five entropies which were integrated as a set of entropy domain descriptors (EDDs) to illustrate the changes in the complexity of EEGs. Second, the combination EDD set was used to develop a customized EEG framework by estimating the entropy-spatial descriptors (ESDs) set for identifying gender from emotional-based EEGs. The proposed methods were validated on EEGs of 30 participants who examined short emotional video clips with four audio-visual stimuli (anger, happiness, sadness, and neutral). The individual performance of computed entropies was statistically examined using analysis of variance (ANOVA) to identify a gender role in the brain emotions. Finally, the proposed ESD framework performance was evaluated using three classifiers: support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), and long short-term memory (LSTM) deep learning model. The results illustrated the effect of individual EDD features as remarkable indices for investigating gender while studying the relationship between EEG brain activity and emotional state changes. Moreover, the proposed ESD achieved significant enhancement in classification accuracy with SVM indicating that ESD may offer a helpful path for reliable improvement of the gender detection from emotional-based EEGs.
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Khare SK, Bajaj V. A self-learned decomposition and classification model for schizophrenia diagnosis. Comput Methods Programs Biomed 2021; 211:106450. [PMID: 34619600 DOI: 10.1016/j.cmpb.2021.106450] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Schizophrenia (SZ) is a type of neurological disorder that is diagnosed by professional psychiatrists based on interviews and manual screening of patients. The procedures are time-consuming, burdensome, and prone to human error. This urgently necessitates the development of an effective and precise computer-aided design for the detection of SZ. One such efficient source for SZ detection is the electroencephalogram (EEG) signals. Because EEG signals are non-stationary, it is challenging to find representative information in its raw form. Decomposing the signals into multi-modes can provide detailed insight information from it. But the choice of uniform decomposition and hyper-parameters leads to information loss affecting system performance drastically. METHOD In this paper, automatic signal decomposition and classification methods are proposed for the detection of SZ and healthy control EEG signals. The Fisher score method is used for the selection of the most discriminant channel. Flexible tunable Q wavelet transform (F-TQWT) is developed for efficient decomposition of EEG signals by reducing root mean square error with grey wolf optimization (GWO) algorithm. Five features are extracted from the adaptively generated subbands and selected by the Kruskal Wallis test. The feature matrix is given as an input to the flexible least square support vector machine (F-LSSVM) classifier. The hyper-parameters and kernel of classifier are selected such that the accuracy of each subband is maximized using GWO algorithm. RESULTS The effectiveness and superiority of the proposed method is tested by evaluating seven performance parameters. An accuracy of 91.39%, sensitivity, specificity, precision, F-1 measure, false positive rate and error of 92.65%, 93.22%, 95.57%, 0.9306, 6.78% and 8.61% is achieved. The results prove superiority of the developed F-TQWT decomposition and F-LSSVM classifier over existing methodologies. CONCLUSION The EEG signals of healthy control and SZ subjects performing motor and auditory tasks simultaneously provide higher discrimination ability over the subjects performing auditory and motory tasks separately. The developed model is accurate, robust, and effective as it is developed on a relatively larger data-set, obtained maximum performance, and tested using ten-fold cross-validation technique. This proposed model is ready to be put to test for real-time SZ detection.
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Affiliation(s)
- Smith K Khare
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India.
| | - Varun Bajaj
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India
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Maheshwari D, Ghosh SK, Tripathy RK, Sharma M, Acharya UR. Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals. Comput Biol Med 2021; 134:104428. [PMID: 33984749 DOI: 10.1016/j.compbiomed.2021.104428] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 10/21/2022]
Abstract
Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with β-rhythm has obtained the accuracy values of 98.91%, 98.45%, and 98.69% for LV vs. HV, LA vs. HA, and LD vs. HD emotion classification strategies, respectively using DEAP database with 10-fold cross-validation (CV) scheme. Similarly, the accuracy values of 98.56%, 98.82%, and 98.99% are obtained for LV vs. HV, LA vs. HA, and LD vs. HD classification schemes, respectively, using deep CNN and θ-rhythm. The proposed multi-channel rhythm-specific deep CNN classification model has obtained the average accuracy value of 57.14% using α-rhythm and trial-specific CV using DASPS database. Moreover, for 8-quadrant based emotion classification strategy, the deep CNN based classifier has obtained an overall accuracy value of 24.37% using γ-rhythms of multi-channel EEG signals. Our developed deep CNN model can be used for real-time automated emotion recognition applications.
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Affiliation(s)
- Daksh Maheshwari
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India
| | - S K Ghosh
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India
| | - R K Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
| | - Manish Sharma
- Department of Electrical and Computer Science Engineering, IITRAM, Ahmedabad, India
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
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Ein Shoka AA, Alkinani MH, El-Sherbeny AS, El-Sayed A, Dessouky MM. Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals. Brain Inform 2021; 8:1. [PMID: 33580323 PMCID: PMC7881082 DOI: 10.1186/s40708-021-00123-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 01/10/2021] [Indexed: 11/18/2022] Open
Abstract
Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.
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Affiliation(s)
- Athar A Ein Shoka
- Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Monagi H Alkinani
- Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - A S El-Sherbeny
- Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Ayman El-Sayed
- Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Mohamed M Dessouky
- Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt. .,Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
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Bolner F, Magits S, van Dijk B, Wouters J. Precompensating for spread of excitation in a cochlear implant coding strategy. Hear Res 2020; 395:107977. [PMID: 32653106 DOI: 10.1016/j.heares.2020.107977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 03/11/2020] [Accepted: 04/15/2020] [Indexed: 11/22/2022]
Abstract
Cochlear implant users' limited ability to understand speech in noisy environments has been linked to the poor spatial resolution and the high degree of spectral smearing associated with the spread of neural excitation. A sound coding algorithm that aims to improve the spectro-temporal representation of the sound signal at the implanted ear by precompensating the electrical stimulation for the spread of excitation is presented in this study. The spread precompensation algorithm was integrated into the standard clinical advanced combination encoder (ACE) strategy and the resulting strategy was called SPACE. SPACE was evaluated acutely with a group of six implant users and was compared to their daily used ACE strategy in terms of preference rating and speech recognition in four-talker babble and stationary speech-shaped noise. While no significant differences in preference rating were observed, speech recognition in four-talker babble was improved by SPACE processing. Analysis of the group results revealed a significant improvement in mean speech reception threshold (SRT) over the ACE strategy of 1.4 dB in four-talker babble, whereas the difference of 0.9 dB in stationary noise did not reach statistical significance. Assessment of individual differences showed that four out of six listeners obtained significant SRT improvements with SPACE and that no subject scored significantly worse compared to ACE. The results suggest that the proposed sound coding strategy has the potential to improve speech perception for cochlear implant users in challenging listening situations.
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Sun H, Jin J, Kong W, Zuo C, Li S, Wang X. Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn 2021; 15:141-56. [PMID: 33786085 DOI: 10.1007/s11571-020-09608-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 05/09/2020] [Accepted: 06/13/2020] [Indexed: 11/27/2022] Open
Abstract
Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.
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Yan M, Lv Z, Sun W, Bi N. An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition. Med Eng Phys 2020; 83:130-41. [PMID: 32475767 DOI: 10.1016/j.medengphy.2020.05.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/24/2020] [Accepted: 05/13/2020] [Indexed: 11/21/2022]
Abstract
Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalography (EEG)-based emotional HCI, this paper proposes an improved common spatial pattern combined with a channel-selection strategy (ICSPCS) for EEG-based emotion recognition. Specifically, we first use a common spatial pattern algorithm to design a spatial domain filter according to three different emotions (positive, neutral, and negative). The traditional joint approximation diagonalization method using the criterion of the "highest score eigenvalue" may be unable to solve multiple classifications of emotion representation. Therefore, we design three different eigenvalue selection methods in terms of the positions of the eigenvalues with the highest scores. Finally, to make the installation easier and reduce the computational load, we also develop a channel-selection strategy based on the weight values that individually reflect the degrees of influence of all the channels on emotion recognition. Experiments are conducted on a self-collected dataset and on the MAHNOB-HCI dataset. The average recognition accuracies for the three emotion tasks are found to be 85.85% and 94.13% on the self-collected and MAHNOB-HCI datasets, respectively, thus proving the effectiveness of the proposed ICSPCS method for emotion recognition.
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Jin J, Miao Y, Daly I, Zuo C, Hu D, Cichocki A. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw 2019; 118:262-270. [PMID: 31326660 DOI: 10.1016/j.neunet.2019.07.008] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 06/18/2019] [Accepted: 07/07/2019] [Indexed: 10/26/2022]
Abstract
Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks. Based on this hypothesis, a correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study. The aim is to improve the classification performance of MI-based BCIs. Furthermore, a novel regularized common spatial pattern (RCSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with the Radial Basis Function (RBF) kernel is trained to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) to validate the effectiveness of the proposed methods. The results show that the CCS algorithm obtained superior classification accuracy (78% versus 56.4% for dataset1, 86.6% versus 76.5% for dataset 2 and 91.3% versus 85.1% for dataset 3) compared to the algorithm using all channels (AC), when CSP is used to extract the features. Furthermore, RCSP could further improve the classification accuracy (81.6% for dataset1, 87.4% for dataset2 and 91.9% for dataset 3), when CCS is used to select the channels.
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Affiliation(s)
- Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China.
| | - Yangyang Miao
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Cili Zuo
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China
| | - Dewen Hu
- College of Mechatronic Engineering and Automation, National University of Defense Technology Changsha, Hunan 410073, PR China
| | - Andrzej Cichocki
- Skolkowo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia; Systems Research Institute PAS, Warsaw, Poland; Nicolaus Copernicus University (UMK), Torun, Poland
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Aydemir O, Ergün E. A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces. J Neurosci Methods 2018; 313:60-67. [PMID: 30529410 DOI: 10.1016/j.jneumeth.2018.12.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/05/2018] [Accepted: 12/05/2018] [Indexed: 10/27/2022]
Abstract
BACKGROUND The input signals of electroencephalography (EEG) based brain computer interfaces (BCI) are extensively acquired from scalp with a multi-channel system. However, multi-channel signals might contain redundant information and increase computational complexity. Furthermore, using only effective channels, rather than all channels, may enhance the performance of the BCI in terms of classification accuracy (CA). NEW METHOD We proposed a robust and subject-specific sequential forward search method (RSS-SFSM) for effective channel selection (ECS). The ECS procedure executes a sequential search among each of the candidate channels in order to find the channels which maximize the CA performance of the validation set. It should be noted that in order to avoid the problems of random selections in the validation set, we applied the ECS procedure for 100 times. Then, the total numbers of the selection of each channel present the effective ones. To demonstrate its reliability and robustness, the proposed method was applied to two data sets. RESULTS The achieved results showed that the proposed method not only improved the average CA by 15.98%, but also decreased the considered number of channels and computational complexity by 71.53% on average. COMPARISON WITH EXISTING METHOD(S) Compared with the existing methods, we achieved better results in terms of both the classification accuracy improvement and channel reduction rates. CONCLUSIONS Features extracted by Hilbert transform and sum derivative methods were effectively classified by support vector machine. In conclusion, the results obtained proved that the RSS-SFSM shows great potential for determining effective channel(s).
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Affiliation(s)
- Onder Aydemir
- Department of Electrical and Electronics Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey.
| | - Ebru Ergün
- Department of Electrical and Electronics Engineering, Recep Tayyip Erdoğan University, 53100, Rize, Turkey.
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Ruan J, Wu X, Zhou B, Guo X, Lv Z. An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface. J Med Syst 2018; 42:253. [PMID: 30402801 DOI: 10.1007/s10916-018-1106-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 10/18/2018] [Indexed: 11/26/2022]
Abstract
Independent component analysis (ICA) is a potential spatial filtering method for the implementation of motor imagery brain-computer interface (MIBCI). However, ICA-based MIBCI (ICA-MIBCI) is sensitive to electroencephalogram (EEG) channels and the quality of the training data, which are two crucial factors affecting the stability and classification performance of ICA-MIBCI. To address these problems, this paper is mainly focused on the investigation of EEG channel optimization. As a reference, we constructed a single-trial-based ICA-MIBCI system with commonly used channels and common spatial pattern-based MIBCI (CSP-MIBCI). To minimize the impact of artifacts on EEG channel optimization, a data-quality evaluation method, named "self-testing" in this paper, was used in a single-trial-based ICA-MIBCI system to evaluate the quality of single trials in each dataset; the resulting self-testing accuracies were used for the selection of high-quality trials. Given several candidate channel configurations, ICA filters were calculated using selected high-quality trials and applied to the corresponding ICA-MIBCI implementation. Optimal channels for each dataset were assessed and selected according to the self-testing results related to various candidate configurations. Forty-eight MI datasets of six subjects were employed in this study to validate the proposed methods. Experimental results revealed that the average classification accuracy of the optimal channels yielded a relative increment of 2.8% and 8.5% during self-testing, 14.4% and 9.5% during session-to-session transfer, and 36.2% and 26.7% during subject-to-subject transfer compared to CSP-MIBCI and ICA-MIBCI with fixed the channel configuration. This work indicates that the proposed methods can efficiently improve the practical feasibility of ICA-MIBCI.
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Wang H, Dragomir A, Abbasi NI, Li J, Thakor NV, Bezerianos A. A novel real-time driving fatigue detection system based on wireless dry EEG. Cogn Neurodyn 2018; 12:365-376. [PMID: 30137873 DOI: 10.1007/s11571-018-9481-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 11/15/2017] [Accepted: 02/16/2018] [Indexed: 11/30/2022] Open
Abstract
Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density (PSD) and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the θ (4-7 Hz), α (8-12 Hz) and β (13-30 Hz) bands frequency components for calculating corresponding PSD of the selected channels. In order to improve the fatigue detection performance, the system was individually calibrated for each subject in terms of fatigue-sensitive channels selection. Two fatigue-related indexes: ( θ+α )/ β and θ / β were computed and then fused into an integrated metric to predict the degree of driving fatigue. In the case of SE extraction, the mean of SE averaged across two EEG channels ('O1h' and 'O2h') was used for fatigue detection. Ten healthy subjects participated in our study and each of them performed two sessions of simulated driving. In each session, subjects were required to drive simulated car for 90 min without any break. The results demonstrate that our proposed methods are effective for fatigue detection. The prediction of fatigue is consistent with the observation of reaction time that was recorded during simulated driving, which is considered as an objective behavioral measure.
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Affiliation(s)
- Hongtao Wang
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore.,2School of Information Engineering, Wuyi University, Jiangmen, 529020 Guangdong China
| | - Andrei Dragomir
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
| | - Nida Itrat Abbasi
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore.,3Department of Biomedical Engineering, National University of Singapore, Singapore, 117456 Singapore
| | - Junhua Li
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
| | - Nitish V Thakor
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
| | - Anastasios Bezerianos
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
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Abstract
Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.
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Affiliation(s)
- Mehmet Siraç Özerdem
- Electrical and Electronics Engineering, Dicle University, 21000, Diyarbakır, Turkey
| | - Hasan Polat
- Electrical and Electronics Engineering, Mus Alparslan University, 49000, Muş, Turkey.
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Ludwig SA, Kong J. Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface. Med Biol Eng Comput 2017; 55:2143-2154. [PMID: 28553694 DOI: 10.1007/s11517-017-1658-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 05/13/2017] [Indexed: 12/01/2022]
Abstract
Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs.
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Affiliation(s)
- Simone A Ludwig
- Department of Computer Science, North Dakota State University, Fargo, ND, USA.
| | - Jun Kong
- Department of Computer Science, North Dakota State University, Fargo, ND, USA
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Al Harrach M, Boudaoud S, Hassan M, Ayachi FS, Gamet D, Grosset JF, Marin F. Denoising of HD-sEMG signals using canonical correlation analysis. Med Biol Eng Comput 2017; 55:375-88. [PMID: 27221811 DOI: 10.1007/s11517-016-1521-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 05/04/2016] [Indexed: 10/21/2022]
Abstract
High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.
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Colwell KA, Ryan DB, Throckmorton CS, Sellers EW, Collins LM. Channel selection methods for the P300 Speller. J Neurosci Methods 2014; 232:6-15. [PMID: 24797224 PMCID: PMC4106671 DOI: 10.1016/j.jneumeth.2014.04.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Revised: 03/06/2014] [Accepted: 04/10/2014] [Indexed: 11/30/2022]
Abstract
The P300 Speller brain-computer interface (BCI) allows a user to communicate without muscle activity by reading electrical signals on the scalp via electroencephalogram. Modern BCI systems use multiple electrodes ("channels") to collect data, which has been shown to improve speller accuracy; however, system cost and setup time can increase substantially with the number of channels in use, so it is in the user's interest to use a channel set of modest size. This constraint increases the importance of using an effective channel set, but current systems typically utilize the same channel montage for each user. We examine the effect of active channel selection for individuals on speller performance, using generalized standard feature-selection methods, and present a new channel selection method, termed jumpwise regression, that extends the Stepwise Linear Discriminant Analysis classifier. Simulating the selections of each method on real P300 Speller data, we obtain results demonstrating that active channel selection can improve speller accuracy for most users relative to a standard channel set, with particular benefit for users who experience low performance using the standard set. Of the methods tested, jumpwise regression offers accuracy gains similar to the best-performing feature-selection methods, and is robust enough for online use.
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Affiliation(s)
- K A Colwell
- Department of Electrical & Computer Engineering, Duke University, Durham, NC, USA.
| | - D B Ryan
- Department of Psychology, East Tennessee State University, Johnson City, TN, USA.
| | - C S Throckmorton
- Department of Electrical & Computer Engineering, Duke University, Durham, NC, USA.
| | - E W Sellers
- Department of Psychology, East Tennessee State University, Johnson City, TN, USA.
| | - L M Collins
- Department of Electrical & Computer Engineering, Duke University, Durham, NC, USA.
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