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Kilani S, Aghili SN, Fathi Y, Sburlea AI. Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces. Front Neurosci 2024; 18:1360709. [PMID: 39071181 PMCID: PMC11272559 DOI: 10.3389/fnins.2024.1360709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 06/25/2024] [Indexed: 07/30/2024] Open
Abstract
Introduction Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples. Methods To address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection. Results The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <½ of the training samples needed by previous studies. Discussion Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.
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Affiliation(s)
- Sepideh Kilani
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Seyedeh Nadia Aghili
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Yaser Fathi
- Institute of Neuroscience, Universite Catholique de Louvain, Brussels, Belgium
| | - Andreea Ioana Sburlea
- Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands
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Chugh N, Aggarwal S, Balyan A. The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG. Clin EEG Neurosci 2024; 55:22-33. [PMID: 37682533 DOI: 10.1177/15500594231193511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.
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Affiliation(s)
- Nupur Chugh
- Netaji Subhas Institute of Technology, New Delhi, India
| | - Swati Aggarwal
- Netaji Subhas University of Technology, New Delhi, India
| | - Arnav Balyan
- Netaji Subhas Institute of Technology, New Delhi, India
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Havaei P, Zekri M, Mahmoudzadeh E, Rabbani H. An efficient deep learning framework for P300 evoked related potential detection in EEG signal. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107324. [PMID: 36586179 DOI: 10.1016/j.cmpb.2022.107324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 11/20/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as "TGT-MHOG-CNN" for detection of P300 ERP in EEG signal. METHOD In the proposed method, GT is tuned based on triangular function for EEG signals, and then spectrograms including time-frequency information are captured. The function's parameters are justified to differentiate the signals with the P300 component. Furthermore, HOG is modified (MHOG) for the 2-D EEG signal, and consequently, gradients patterns are extracted for the target potentials. MHOG is potent in distinguishing the positive peak in the general waveform; however, GT unravels time-frequency information, which is ignored in the gradient histogram. These outputs of GT and MHOG do not share the same nature in the images nor overlap. Therefore, more extensive information is reached without redundancy or excessive information by fusing them. Combining GT and MHOG provides different patterns which benefit CNN for more precise detection. Consequently, TGT-MHOG-CNN ends in a more straightforward structure than other networks, and therefore, the whole performance is acceptable with faster rates and very high accuracy. RESULTS BCI Competition II and III datasets are used to evaluate the performance of the proposed method. These datasets include a complete record for P300 ERP with BCI2000 using a paradigm, and it has numerous noises, including power and muscle-based noises. The objective is to predict the correct character in each provided character selection epochs. Compared to state-of-the-art methods, simulation results indicate striking abilities of the proposed framework for P300 ERP detection. Our best record reached the P300 ERP classification rates of over 98.7% accuracy and 98.7% precision for BCI Competition II and 99% accuracy and 100% precision for BCI Competition III datasets, with superiority in execution time for the mentioned datasets.
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Affiliation(s)
- Pedram Havaei
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Maryam Zekri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Elham Mahmoudzadeh
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; School of Advanced Technologies In Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
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Blanco-Díaz CF, Guerrero-Méndez CD, Ruiz-Olaya AF. Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller. Ing Rech Biomed 2023. [DOI: 10.1016/j.irbm.2022.100751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Li Q, Wu Y, Song Y, Zhao D, Sun M, Zhang Z, Wu J. A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network. Front Comput Neurosci 2022; 16:909553. [PMID: 35782086 PMCID: PMC9243506 DOI: 10.3389/fncom.2022.909553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/13/2022] [Indexed: 11/29/2022] Open
Abstract
Background Electroencephalogram (EEG)-based brain-computer interface (BCI) systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. P300, the main event-related potential, is the primary component detected by EEG-based BCI systems. Existing algorithms for P300 classification in EEG data usually perform well when tested in a single participant, although they exhibit significant decreases in accuracy when tested in new participants. We attempted to address this lack of generalizability associated with existing classification methods using a novel convolutional neural network (CNN) model developed using logistic regression (LR). Materials and Methods We proposed an LR-CNN model comprising two parts: a combined LR-based memory model and a CNN-based generalization model. The LR-based memory model can learn the individual features of participants and addresses the decrease in accuracy caused by individual differences when applied to new participants. The CNN-based generalization model can learn the common features among participants, thereby reducing overall classification bias and improving overall classification accuracy. Results We compared our method with existing, commonly used classification methods through three different sets of experiments. The experimental results indicated that our method could learn individual differences among participants. Compared with other commonly used classification methods, our method yielded a marked improvement (>90%) in classification among new participants. Conclusion The accuracy of the proposed model in the face of new participants is better than that of existing, commonly used classification methods. Such improvements in cross-subject test accuracy will aid in the development of BCI systems.
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Affiliation(s)
- Qi Li
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
- Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China
- *Correspondence: Qi Li,
| | - Yan Wu
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Yu Song
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Di Zhao
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Meiqi Sun
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Zhilin Zhang
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Zhilin Zhang,
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Zhang Z, Sun J, Chen T. A new dynamically convergent differential neural network for brain signal recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103130] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Mussabayeva A, Jamwal PK, Tahir Akhtar M. Ensemble Learning Approach for Subject-Independent P300 Speller. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5893-5896. [PMID: 34892460 DOI: 10.1109/embc46164.2021.9629679] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
P300 speller is a brain-computer interface (BCI) speller system, used for enabling human with different paralyzing disorders, such as amyotrophic lateral sclerosis (ALS), to communicate with the outer world by processing electroencephalography (EEG) signals. Different people have different latency and amplitude of the P300 event-related potential (ERP) component, which is used as the main feature for detecting the target character. In order to achieve robust results for different subjects using generic training (GT), the ensemble learning classifiers are proposed based on linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and convolutional neural network (CNN). The proposed models are trained using data from healthy subjects and tested on both healthy subjects and ALS patients. The results show that the fusion of LDA, kNN and SVM provides the most accurate results, achieving the accuracy of 99% for healthy subjects and about 85% for ALS patients.
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Li S, Jin J, Daly I, Wang X, Lam HK, Cichocki A. Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method. J Neurosci Methods 2021; 362:109300. [PMID: 34343575 DOI: 10.1016/j.jneumeth.2021.109300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/14/2021] [Accepted: 07/29/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability. NEW METHODS In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention. RESULTS The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance. COMPARISON WITH EXISTING METHODS The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms. CONCLUSIONS The proposed MSFF method is able to improve the performance of P300-based BCIs.
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Affiliation(s)
- Shurui Li
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Hak-Keung Lam
- Department of Engineering, King's College London, London WC2R 2LS, UK
| | - Andrzej Cichocki
- Skolkovo 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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10
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Olsen S, Zhang J, Liang KF, Lam M, Riaz U, Kao JC. An artificial intelligence that increases simulated brain-computer interface performance. J Neural Eng 2021; 18. [PMID: 33978599 DOI: 10.1088/1741-2552/abfaaa] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/22/2021] [Indexed: 12/14/2022]
Abstract
Objective.Brain-computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard.Approach.Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI's trajectories.Main results.We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to 'dial in' on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control.Significance.This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.
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Affiliation(s)
- Sebastian Olsen
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Jianwei Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Ken-Fu Liang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Michelle Lam
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Usama Riaz
- Department of Computer Science, University of California, Los Angeles, CA 90024, United States of America
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America.,Neurosciences Program, University of California, Los Angeles, CA 90024, United States of America
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Katyal EA, Singla R. EEG-based hybrid QWERTY mental speller with high information transfer rate. Med Biol Eng Comput 2021; 59:633-661. [PMID: 33594631 DOI: 10.1007/s11517-020-02310-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 12/30/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller. METHODS The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy. RESULTS On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F). COMPARISON The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller. CONCLUSION QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.
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Affiliation(s)
- Er Akshay Katyal
- ICE Department, Dr B.R. Ambedkar N.I.T. Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144011, India.
| | - Rajesh Singla
- ICE Department, Dr B.R. Ambedkar N.I.T. Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144011, India
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Touati R, Tajouri A, Mesaoudi I, Oueslati AE, Lachiri Z, Kharrat M. New methodology for repetitive sequences identification in human X and Y chromosomes. Biomed Signal Process Control 2021; 64:102207. [PMID: 33101452 PMCID: PMC7572123 DOI: 10.1016/j.bspc.2020.102207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 07/23/2020] [Accepted: 09/01/2020] [Indexed: 11/24/2022]
Abstract
Repetitive DNA sequences occupy the major proportion of DNA in the human genome and even in the other species' genomes. The importance of each repetitive DNA type depends on many factors: structural and functional roles, positions, lengths and numbers of these repetitions are clear examples. Conserving such DNA sequences or not in different locations in the chromosome remains a challenge for researchers in biology. Detecting their location despite their great variability and finding novel repetitive sequences remains a challenging task. To side-step this problem, we developed a new method based on signal and image processing tools. In fact, using this method we could find repetitive patterns in DNA images regardless of the repetition length. This new technique seems to be more efficient in detecting new repetitive sequences than bioinformatics tools. In fact, the classical tools present limited performances especially in case of mutations (insertion or deletion). However, modifying one or a few numbers of pixels in the image doesn't affect the global form of the repetitive pattern. As a consequence, we generated a new repetitive patterns database which contains tandem and dispersed repeated sequences. The highly repetitive sequences, we have identified in X and Y chromosomes, are shown to be located in other human chromosomes or in other genomes. The data we have generated is then taken as input to a Convolutional neural network classifier in order to classify them. The system we have constructed is efficient and gives an average of 94.4% as recognition score.
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Affiliation(s)
- Rabeb Touati
- University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Asma Tajouri
- University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia
| | - Imen Mesaoudi
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Afef Elloumi Oueslati
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Zied Lachiri
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Maher Kharrat
- University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia
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Liao H, Xu J, Yu Z. Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection. ENTROPY (BASEL, SWITZERLAND) 2020; 23:E39. [PMID: 33383909 PMCID: PMC7823555 DOI: 10.3390/e23010039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/23/2020] [Accepted: 12/24/2020] [Indexed: 11/17/2022]
Abstract
In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.
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Affiliation(s)
- Hongpeng Liao
- College of Automation Science and Technology, South China University of Technology, Guangzhou 510641, China;
| | - Jianwu Xu
- Guangzhou Galaxy Thermal Energy Incorporated Company, Guangzhou 510220, China;
| | - Zhuliang Yu
- College of Automation Science and Technology, South China University of Technology, Guangzhou 510641, China;
- Pazhou Lab., Guangzhou 510330, China
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Kundu S, Ari S. MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller. ACTA ACUST UNITED AC 2020. [DOI: 10.1109/tmrb.2019.2959559] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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