51
|
Ma P, Dong C, Lin R, Ma S, Jia T, Chen X, Xiao Z, Qi Y. A classification algorithm of an SSVEP brain-computer interface based on CCA fusion wavelet coefficients. J Neurosci Methods 2022; 371:109502. [DOI: 10.1016/j.jneumeth.2022.109502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 11/16/2022]
|
52
|
Yan W, Xu G, Du Y, Chen X. SSVEP-EEG Feature Enhancement Method Using an Image Sharpening Filter. IEEE Trans Neural Syst Rehabil Eng 2022; 30:115-123. [PMID: 35025745 DOI: 10.1109/tnsre.2022.3142736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis.
Collapse
|
53
|
Zhou Y, Hu L, Yu T, Li Y. A BCI-Based Study on the Relationship Between the SSVEP and Retinal Eccentricity in Overt and Covert Attention. Front Neurosci 2022; 15:746146. [PMID: 34970111 PMCID: PMC8712654 DOI: 10.3389/fnins.2021.746146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 12/04/2022] Open
Abstract
Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them.
Collapse
Affiliation(s)
- Yajun Zhou
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| | - Li Hu
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| | - Tianyou Yu
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| | - Yuanqing Li
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| |
Collapse
|
54
|
Davarinia F, Maleki A. SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
55
|
Liang L, Bin G, Chen X, Wang Y, Gao S, Gao X. Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear. J Neural Eng 2021; 18. [PMID: 34875637 DOI: 10.1088/1741-2552/ac40a1] [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: 08/27/2021] [Accepted: 12/07/2021] [Indexed: 11/12/2022]
Abstract
Objective.Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region.Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.Main results.In the 12-target online experiment, after a short model estimation training, all 16 subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6 ± 20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2±14.7%, and the information transfer rate from 14.2±6.4 bits min-1to 17.8±5.7 bits min-1.Significance.These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.
Collapse
Affiliation(s)
- Liyan Liang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Guangyu Bin
- Department of Biomedical Engineering, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, People's Republic of China
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China
| | - Shangkai Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| |
Collapse
|
56
|
Jorajuría T, Jamshidi Idaji M, İşcan Z, Gómez M, Nikulin VV, Vidaurre C. Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
57
|
Bolanos MC, Barrado Ballestero S, Puthusserypady S. Filter bank approach for enhancement of supervised Canonical Correlation Analysis methods for SSVEP-based BCI spellers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:337-340. [PMID: 34891304 DOI: 10.1109/embc46164.2021.9630838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Canonical correlation analysis (CCA) is one of the most used algorithms in the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems due to its simplicity, efficiency, and robustness. Researchers have proposed modifications to CCA to improve its speed, allowing high-speed spelling and thus a more natural communication. In this work, we combine two approaches, the filter-bank (FB) approach to extract more information from the harmonics, and a range of different supervised methods which optimize the reference signals to improve the SSVEP detection. The proposed models are tested on the publicly available benchmark dataset for SSVEP-based BCIs and the results show improved performance compared to the state-of-the-art methods and, in particular, the proposed FBMwayCCA approach achieves the best results with an information transfer rate (ITR) of 134.8±8.4 bits/minute. This study indeed suggests the feasibility of combining the fundamental and harmonic SSVEP components with supervised methods in target identification to develop high-speed BCI spellers.
Collapse
|
58
|
Ming G, Pei W, Chen H, Gao X, Wang Y. Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs. J Neural Eng 2021; 18. [PMID: 34544060 DOI: 10.1088/1741-2552/ac284a] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/20/2021] [Indexed: 11/12/2022]
Abstract
Objective.Low-frequency steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems with high performance are prone to cause visual discomfort and fatigue. High-frequency SSVEP-based BCI systems can alleviate the discomfort, but always obtain lower performance. This study optimized the spatial properties of a proposed checkerboard-like visual stimulus toward high-performance and user-friendly SSVEP-based BCI systems.Approach.On the one hand, two checkerboard-like stimuli with distinct spatial contrasts (the black- and white-background) were designed to balance the tradeoff between BCI performance and user experience and compared with the traditional flickering stimulus. On the other hand, the impacts of the spatial frequency of the new checkerboard-like stimulus on the flicker perception and the intensity of the elicited SSVEP were clarified. The SSVEP-based BCI systems were implemented based on the checkerboard-like stimuli under low-frequency and high-frequency conditions. The user experience for each stimulation pattern was estimated by questionnaires for subjective evaluation.Main results.The comparison results indicate that the black-background checkerboard-like stimulus with an optimized spatial frequency achieved comparable performance and enhanced visual comfort compared with the flickering stimulus. Furthermore, the online nine-target BCI system using the black-background checkerboard-like stimuli achieved averaged information transfer rates of 124.0 ± 2.3 and 109.0 ± 20.4 bits min-1with low-frequency and high-frequency stimulation respectively.Significance.The new checkerboard-like stimuli with optimized properties show superiority of system performance and user experience in implementing SSVEP-based BCI, which will promote its practical applications in communication and control.
Collapse
Affiliation(s)
- Gege Ming
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Weihua Pei
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Hongda Chen
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, People's Republic of China.,College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| |
Collapse
|
59
|
Ingel A, Vicente R. Information Bottleneck as Optimisation Method for SSVEP-Based BCI. Front Hum Neurosci 2021; 15:675091. [PMID: 34557078 PMCID: PMC8452926 DOI: 10.3389/fnhum.2021.675091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 06/04/2021] [Indexed: 11/13/2022] Open
Abstract
In this study, the information bottleneck method is proposed as an optimisation method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The information bottleneck is an information-theoretic optimisation method for solving problems with a trade-off between preserving meaningful information and compression. Its main practical application in machine learning is in representation learning or feature extraction. In this study, we use the information bottleneck to find optimal classification rule for a BCI. This is a novel application for the information bottleneck. This approach is particularly suitable for BCIs since the information bottleneck optimises the amount of information transferred by the BCI. Steady-state visual evoked potential-based BCIs often classify targets using very simple rules like choosing the class corresponding to the largest feature value. We call this classifier the arg max classifier. It is unlikely that this approach is optimal, and in this study, we propose a classification method specifically designed to optimise the performance measure of BCIs. This approach gives an advantage over standard machine learning methods, which aim to optimise different measures. The performance of the proposed algorithm is tested on two publicly available datasets in offline experiments. We use the standard power spectral density analysis (PSDA) and canonical correlation analysis (CCA) feature extraction methods on one dataset and show that the current approach outperforms most of the related studies on this dataset. On the second dataset, we use the task-related component analysis (TRCA) method and demonstrate that the proposed method outperforms the standard argmax classification rule in terms of information transfer rate when using a small number of classes. To our knowledge, this is the first time the information bottleneck is used in the context of SSVEP-based BCIs. The approach is unique in the sense that optimisation is done over the space of classification functions. It potentially improves the performance of BCIs and makes it easier to calibrate the system for different subjects.
Collapse
Affiliation(s)
- Anti Ingel
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Raul Vicente
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| |
Collapse
|
60
|
Liu B, Chen X, Shi N, Wang Y, Gao S, Gao X. Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1998-2007. [PMID: 34543200 DOI: 10.1109/tnsre.2021.3114340] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.
Collapse
|
61
|
Zheng X, Xu G, Han C, Tian P, Zhang K, Liang R, Jia Y, Yan W, Du C, Zhang S. Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering. Front Neurosci 2021; 15:716051. [PMID: 34489633 PMCID: PMC8417433 DOI: 10.3389/fnins.2021.716051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/20/2021] [Indexed: 11/30/2022] Open
Abstract
The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.
Collapse
Affiliation(s)
- Xiaowei Zheng
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Guanghua Xu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Chengcheng Han
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Peiyuan Tian
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Kai Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Renghao Liang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yaguang Jia
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Wenqiang Yan
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Chenghang Du
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Sicong Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| |
Collapse
|
62
|
Guney OB, Oblokulov M, Ozkan H. A Deep Neural Network for SSVEP-based Brain-Computer Interfaces. IEEE Trans Biomed Eng 2021; 69:932-944. [PMID: 34495825 DOI: 10.1109/tbme.2021.3110440] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture. METHOD The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities. RESULTS Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI. CONCLUSION The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets. SIGNIFICANCE Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.
Collapse
|
63
|
Yan W, Du C, Wu Y, Zheng X, Xu G. SSVEP-EEG Denoising via Image Filtering Methods. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1634-1643. [PMID: 34398754 DOI: 10.1109/tnsre.2021.3104825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.
Collapse
|
64
|
Liu B, Chen X, Li X, Wang Y, Gao X, Gao S. Align and pool for EEG headset domain adaptation (ALPHA) to facilitate dry electrode based SSVEP-BCI. IEEE Trans Biomed Eng 2021; 69:795-806. [PMID: 34406934 DOI: 10.1109/tbme.2021.3105331] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented in dry electrodes is a promising paradigm for alternative and augmentative communication in real-world applications. To improve its performance and reduce the calibration effort for dry-electrode systems, we utilize cross-device transfer learning by exploiting auxiliary individual wet-electrode electroencephalogram (EEG). METHODS We proposed a novel transfer learning framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the spatial pattern and the covariance for domain adaptation. To evaluate its efficacy, 75 subjects performed an experiment of 2 sessions involving a 12-target SSVEP-BCI task. RESULTS ALPHA significantly outperformed a baseline approach (canonical correlation analysis, CCA) and two competing transfer learning approaches (transfer template CCA, ttCCA and least square transformation, LST) in two transferring directions. When transferring from wet to dry EEG headsets, ALPHA significantly outperformed the fully calibrated approach of task-related component analysis (TRCA). CONCLUSION ALPHA advances the frontier of recalibration-free cross-device transfer learning for SSVEP-BCIs and boosts the performance of dry electrode based systems. SIGNIFICANCE ALPHA has methodological and practical implications and pushes the boundary of dry electrode based SSVEP-BCI toward real-world applications.
Collapse
|
65
|
Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks. SENSORS 2021; 21:s21165309. [PMID: 34450751 PMCID: PMC8398418 DOI: 10.3390/s21165309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/24/2021] [Accepted: 08/03/2021] [Indexed: 11/30/2022]
Abstract
The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.
Collapse
|
66
|
Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis. SENSORS 2021; 21:s21165269. [PMID: 34450713 PMCID: PMC8400839 DOI: 10.3390/s21165269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/29/2021] [Accepted: 08/02/2021] [Indexed: 11/17/2022]
Abstract
Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.
Collapse
|
67
|
Xu L, Xu M, Jung TP, Ming D. Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface. Cogn Neurodyn 2021; 15:569-584. [PMID: 34367361 PMCID: PMC8286913 DOI: 10.1007/s11571-021-09676-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/10/2021] [Accepted: 03/26/2021] [Indexed: 01/04/2023] Open
Abstract
A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.
Collapse
Affiliation(s)
- Lichao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Tzyy-Ping Jung
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Swartz Center for Computational Neuroscience, University of California, San Diego, USA
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| |
Collapse
|
68
|
Shatilov KA, Chatzopoulos D, Lee LH, Hui P. Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up Survey. ACM T INTERACT INTEL 2021. [DOI: 10.1145/3457950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Incremental and quantitative improvements of two-way interactions with e
x
tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR.
Collapse
Affiliation(s)
| | | | - Lik-Hang Lee
- KAIST, Republic of Korea and University of Oulu, Finland
| | - Pan Hui
- Hong Kong University of Science and Technology, Hong Kong and University of Helsinki, Finland
| |
Collapse
|
69
|
Tong C, Wang H, Yang C, Ni X. Group ensemble learning enhances the accuracy and convenience of SSVEP-based BCIs via exploiting inter-subject information. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102797] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
70
|
Singh HP, Kumar P. Developments in the human machine interface technologies and their applications: a review. J Med Eng Technol 2021; 45:552-573. [PMID: 34184601 DOI: 10.1080/03091902.2021.1936237] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Human-machine interface (HMI) techniques use bioelectrical signals to gain real-time synchronised communication between the human body and machine functioning. HMI technology not only provides a real-time control access but also has the ability to control multiple functions at a single instance of time with modest human inputs and increased efficiency. The HMI technologies yield advanced control access on numerous applications such as health monitoring, medical diagnostics, development of prosthetic and assistive devices, automotive and aerospace industry, robotic controls and many more fields. In this paper, various physiological signals, their acquisition and processing techniques along with their respective applications in different HMI technologies have been discussed.
Collapse
Affiliation(s)
- Harpreet Pal Singh
- Department of Mechanical Engineering, Punjabi University, Patiala, India
| | - Parlad Kumar
- Department of Mechanical Engineering, Punjabi University, Patiala, India
| |
Collapse
|
71
|
Singanamalla SKR, Lin CT. Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces. Front Neurosci 2021; 15:651762. [PMID: 33867928 PMCID: PMC8047134 DOI: 10.3389/fnins.2021.651762] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/22/2021] [Indexed: 11/28/2022] Open
Abstract
With the advent of advanced machine learning methods, the performance of brain–computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.
Collapse
Affiliation(s)
- Sai Kalyan Ranga Singanamalla
- Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia
| | - Chin-Teng Lin
- Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.,Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia
| |
Collapse
|
72
|
Li M, He D, Li C, Qi S. Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance. Brain Sci 2021; 11:450. [PMID: 33916189 PMCID: PMC8065759 DOI: 10.3390/brainsci11040450] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/17/2022] Open
Abstract
The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain-computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.
Collapse
Affiliation(s)
- Minglun Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
- Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110169, China
| |
Collapse
|
73
|
Maymandi H, Perez Benitez JL, Gallegos-Funes F, Perez Benitez JA. A novel monitor for practical brain-computer interface applications based on visual evoked potential. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1900032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Hamidreza Maymandi
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| | - Jorge Luis Perez Benitez
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| | - F. Gallegos-Funes
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| | - J. A. Perez Benitez
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| |
Collapse
|
74
|
Asgher U, Khan MJ, Asif Nizami MH, Khalil K, Ahmad R, Ayaz Y, Naseer N. Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI). Front Neurorobot 2021; 15:605751. [PMID: 33815084 PMCID: PMC8012849 DOI: 10.3389/fnbot.2021.605751] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 02/05/2021] [Indexed: 11/24/2022] Open
Abstract
Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier-support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.
Collapse
Affiliation(s)
- Umer Asgher
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Jawad Khan
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Hamza Asif Nizami
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Florida State University College of Engineering, Florida A&M University, Tallahassee, FL, United States
| | - Khurram Khalil
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Riaz Ahmad
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Directorate of Quality Assurance and International Collaboration, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Yasar Ayaz
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI), National University of Sciences and Technology, Islamabad, Pakistan
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan
| |
Collapse
|
75
|
Huang J, Qiu L, Lin Q, Xiao J, Huang Y, Huang H, Zhou X, Shi X, Wang F, He Y, Pan J. Hybrid asynchronous brain-computer interface for yes/no communication in patients with disorders of consciousness. J Neural Eng 2021; 18. [PMID: 33735851 DOI: 10.1088/1741-2552/abf00c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 03/18/2021] [Indexed: 11/12/2022]
Abstract
Objective.For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), communication is challenging. Currently, the communication methods of DOC patients are limited to behavioral responses. However, DOC patients cannot provide sufficient behavioral responses due to motor impairments and limited attention. In this study, we proposed a hybrid asynchronous brain-computer interface (BCI) system that provides a new communication channel for DOC patients.Approach.Seven DOC patients (3 VS and 4 MCS) and eleven healthy subjects participated in our experiment. Each subject was instructed to focus on the square with the Chinese words 'Yes' and 'No'. Then, the BCI system determined the target square with both P300 and steady-state visual evoked potential (SSVEP) detections. For the healthy group, we tested the performance of the hybrid system and the single-modality BCI system.Main results.All healthy subjects achieved significant accuracy (range from 72% to 100%) in both the hybrid system and the single-modality system. The hybrid asynchronous BCI system outperformed the P300-only and SSVEP-only systems. Furthermore, we employed the asynchronous approach to dynamically collect the EEG signals. Compared with the synchronous system, there was a 21% reduction in the average required rounds and a reduction of 105 s in the online experiment time. This asynchronous system was applied to detect the 'yes/no' communication function of seven DOC patients, and the results showed that three of the patients (3 MCS) showed significant accuracies (67 ± 3%) in the online experiment, and their Coma Recovery Scale-Revised (CRS-R) scores were also improved compared with the scores before the experiment. This result demonstrated that 3 of 7 patients were able to communicate using our hybrid asynchronous BCI system.Significance.This hybrid asynchronous BCI system represents a useful auxiliary bedside tool for simple communication with DOC patients.
Collapse
Affiliation(s)
- Jianyong Huang
- South China Normal University, School of Software, Guangzhou, 510630, CHINA
| | - Lina Qiu
- South China Normal University, School of Software, Guangzhou, 510630, CHINA
| | - Qianmin Lin
- Guangdong Work Injury Rehabilitation Hospital, Traumatic Brain Injury Rehabilitation & Severe Rehabilitation Department, Guangzhou, Guangdong, 510400, CHINA
| | - Jun Xiao
- South China University of Technology, Center for Brain Computer Interfaces and Brain Information Processing, Guangzhou, Guangdong, 510640, CHINA
| | - Yuanqiu Huang
- Guangdong Work Injury Rehabilitation Hospital, Traumatic Brain Injury Rehabilitation & Severe Rehabilitation Department, Guangzhou, Guangdong, 510400, CHINA
| | - Haiyun Huang
- South China University of Technology, Center for Brain Computer Interfaces and Brain Information Processing, Guangzhou, Guangdong, 510640, CHINA
| | - Xinjie Zhou
- Guangdong Work Injury Rehabilitation Hospital, Traumatic Brain Injury Rehabilitation & Severe Rehabilitation Department, Guangzhou, Guangdong, 510400, CHINA
| | - Xiangyu Shi
- South China University of Technology, Center for Brain Computer Interfaces and Brain Information Processing, Guangzhou, Guangdong, 510640, CHINA
| | - Fei Wang
- South China Normal University, School of Software, Guangzhou, 510630, CHINA
| | - Yanbin He
- Guangdong Work Injury Rehabilitation Hospital, Traumatic Brain Injury Rehabilitation & Severe Rehabilitation Department, Guangzhou, Guangdong, 510400, CHINA
| | - Jiahui Pan
- South China Normal University, School of Software, Guangzhou, 510631, CHINA
| |
Collapse
|
76
|
Chen Y, Yang C, Chen X, Wang Y, Gao X. A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy. J Neural Eng 2021; 18. [DOI: 10.1088/1741-2552/ab914e] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/07/2020] [Indexed: 11/12/2022]
|
77
|
Abstract
The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.
Collapse
|
78
|
Yan W, Du C, Luo D, Wu Y, Duan N, Zheng X, Xu G. Enhancing detection of steady-state visual evoked potentials using channel ensemble method. J Neural Eng 2021; 18. [PMID: 33601356 DOI: 10.1088/1741-2552/abe7cf] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/18/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs). APPROACH Collected multi-channel electroencephalogram (EEG) signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using the softmax function. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient. MAIN RESULTS Compared with canonical correlation analysis (CCA), likelihood ratio test (LRT), and multivariate synchronization index (MSI) analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain-computer interface (BCI) systems. SIGNIFICANCE A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.
Collapse
Affiliation(s)
- Wenqiang Yan
- Xi'an Jiaotong University School of Mechanical Engineering, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Chenghang Du
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Dan Luo
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Yongcheng Wu
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Nan Duan
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Xiaowei Zheng
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Guanghua Xu
- Xi'an Jiaotong University School of Mechanical Engineering, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| |
Collapse
|
79
|
Lee HK, Choi YS. Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis. SENSORS 2021; 21:s21041315. [PMID: 33673137 PMCID: PMC7918701 DOI: 10.3390/s21041315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/06/2021] [Accepted: 02/09/2021] [Indexed: 11/16/2022]
Abstract
Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.
Collapse
|
80
|
Zhu F, Jiang L, Dong G, Gao X, Wang Y. An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces. SENSORS 2021; 21:s21041256. [PMID: 33578754 PMCID: PMC7916479 DOI: 10.3390/s21041256] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 11/20/2022]
Abstract
Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
Collapse
Affiliation(s)
- Fangkun Zhu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China;
| | - Lu Jiang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guoya Dong
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China;
- Correspondence: (G.D.); (Y.W.)
| | - Xiaorong Gao
- School of Medicine, Tsinghua University, Beijing 100084, China;
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (G.D.); (Y.W.)
| |
Collapse
|
81
|
Shirzhiyan Z, Keihani A, Farahi M, Shamsi E, GolMohammadi M, Mahnam A, Haidari MR, Jafari AH. Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI. Front Neurosci 2020; 14:534619. [PMID: 33328841 PMCID: PMC7718037 DOI: 10.3389/fnins.2020.534619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Visual evoked potentials (VEPs) to periodic stimuli are commonly used in brain computer interfaces for their favorable properties such as high target identification accuracy, less training time, and low surrounding target interference. Conventional periodic stimuli can lead to subjective visual fatigue due to continuous and high contrast stimulation. In this study, we compared quasi-periodic and chaotic complex stimuli to common periodic stimuli for use with VEP-based brain computer interfaces (BCIs). Canonical correlation analysis (CCA) and coherence methods were used to evaluate the performance of the three stimulus groups. Subjective fatigue caused by the presented stimuli was evaluated by the Visual Analogue Scale (VAS). Using CCA with the M2 template approach, target identification accuracy was highest for the chaotic stimuli (M = 86.8, SE = 1.8) compared to the quasi-periodic (M = 78.1, SE = 2.6, p = 0.008) and periodic (M = 64.3, SE = 1.9, p = 0.0001) stimulus groups. The evaluation of fatigue rates revealed that the chaotic stimuli caused less fatigue compared to the quasi-periodic (p = 0.001) and periodic (p = 0.0001) stimulus groups. In addition, the quasi-periodic stimuli led to lower fatigue rates compared to the periodic stimuli (p = 0.011). We conclude that the target identification results were better for the chaotic group compared to the other two stimulus groups with CCA. In addition, the chaotic stimuli led to a less subjective visual fatigue compared to the periodic and quasi-periodic stimuli and can be suitable for designing new comfortable VEP-based BCIs.
Collapse
Affiliation(s)
- Zahra Shirzhiyan
- Computational Neuroscience, Institute of Medical Technology, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.,Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmadreza Keihani
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Morteza Farahi
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Shamsi
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mina GolMohammadi
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Amin Mahnam
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohsen Reza Haidari
- Section of Neuroscience, Department of Neurology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
82
|
Wei Q, Zhu S, Wang Y, Gao X, Guo H, Wu X. A Training Data-Driven Canonical Correlation Analysis Algorithm for Designing Spatial Filters to Enhance Performance of SSVEP-Based BCIs. Int J Neural Syst 2020; 30:2050020. [PMID: 32380925 DOI: 10.1142/s0129065720500203] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.
Collapse
Affiliation(s)
- Qingguo Wei
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - Shan Zhu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, P. R. China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, P. R. China
| | - Hai Guo
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - Xuan Wu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| |
Collapse
|
83
|
Chiang KJ, Wei CS, Nakanishi M, Jung TP. Boosting template-based SSVEP decoding by cross-domain transfer learning. J Neural Eng 2020; 18. [PMID: 33203813 DOI: 10.1088/1741-2552/abcb6e] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/16/2020] [Indexed: 11/12/2022]
Abstract
OBJECTIVE This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential(SSVEP)-based brain-computer interfaces (BCIs) by leveraging cross-domain data transferring. APPROACH We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and EEG montages). MAIN RESULTS Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method. SIGNIFICANCE This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.
Collapse
Affiliation(s)
- Kuan-Jung Chiang
- CSE, University of California San Diego, La Jolla, California, 92093-0021, UNITED STATES
| | - Chun-Shu Wei
- Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Rd, Palo Alto, California, 94304, UNITED STATES
| | - Masaki Nakanishi
- Institute for Neural Computation, University of California, San Diego, California, UNITED STATES
| | - Tzyy-Ping Jung
- University of California San Diego Swartz Center for Computational Neuroscience, San Diego, California, UNITED STATES
| |
Collapse
|
84
|
Xu M, Han J, Wang Y, Jung TP, Ming D. Implementing Over 100 Command Codes for a High-Speed Hybrid Brain-Computer Interface Using Concurrent P300 and SSVEP Features. IEEE Trans Biomed Eng 2020; 67:3073-3082. [DOI: 10.1109/tbme.2020.2975614] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
85
|
Hou H, Zhang X, Meng Q. Olfactory EEG Signal Classification Using a Trapezoid Difference-Based Electrode Sequence Hashing Approach. Int J Neural Syst 2020; 30:2050011. [PMID: 32116092 DOI: 10.1142/s0129065720500112] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain-computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an N-layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on N optimized power-spectral-density features extracted from N real electrodes and N nonreal electrode's features. Subsequently, the N real electrodes' sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen's kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.
Collapse
Affiliation(s)
- Huirang Hou
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - Xiaonei Zhang
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - Qinghao Meng
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China
| |
Collapse
|
86
|
Abstract
Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.
Collapse
Affiliation(s)
- Guang-Li Li
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou 412007, People's Republic of China
| | | | | | | | | |
Collapse
|
87
|
Zhang Y, Gao Q, Song Y, Wang Z. Implementation of an SSVEP-based intelligent home service robot system. Technol Health Care 2020; 29:541-556. [PMID: 33074201 DOI: 10.3233/thc-202442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND People with severe neuromuscular disorders caused by an accident or congenital disease cannot normally interact with the physical environment. The intelligent robot technology offers the possibility to solve this problem. However, the robot can hardly carry out the task without understanding the subject's intention as it relays on speech or gestures. Brain-computer interface (BCI), a communication system that operates external devices by directly converting brain activity into digital signals, provides a solution for this. OBJECTIVE In this study, a noninvasive BCI-based humanoid robotic system was designed and implemented for home service. METHODS A humanoid robot that is equipped with multi-sensors navigates to the object placement area under the guidance of a specific symbol "Naomark", which has a unique ID, and then sends the information of the scanned object back to the user interface. Based on this information, the subject gives commands to the robot to grab the wanted object and give it to the subject. To identify the subject's intention, the channel projection-based canonical correlation analysis (CP-CCA) method was utilized for the steady state visual evoked potential-based BCI system. RESULTS The offline results showed that the average classification accuracy of all subjects reached 90%, and the online task completion rate was over 95%. CONCLUSION Users can complete the grab task with minimum commands, avoiding the control burden caused by complex commands. This would provide a useful assistance means for people with severe motor impairment in their daily life.
Collapse
|
88
|
Wu C, Qiu S, Xing J, He H. A CNN-based compare network for classification of SSVEPs in human walking. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2986-2990. [PMID: 33018633 DOI: 10.1109/embc44109.2020.9176649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain-computer interface (BCI) can provide a way for the disabled to interact with the outside world. Steady-state visual evoked potential (SSVEP), which evokes potential through visual stimulation is one of important BCI paradigms. In laboratory environment, the classification accuracy of SSVEPs is excellent. However, in motion state, the accuracy will be greatly affected and reduce quite a lot. In this paper, in order to improve the classification accuracy of the SSVEP signals in the motion state, we collected SSVEP data of five targets at three speeds of 0km/h, 2.5km/h and 5km/h. A compare network based on convolutional neural network (CNN) was proposed to learn the relationship between EEG signal and the template corresponding to each stimulus frequency and classify. Compared with traditional methods (i.e., CCA, FBCCA and SVM) and state-of-the-art method (CNN) on the collected SSVEP datasets of 20 subjects, the method we proposed always performed best at different speeds. Therefore, these results validated the effectiveness of the method. In addition, compared with the speed of 0 km / h, the accuracy of the compare network at a high walking rate (5km/h) did not decrease much, and it could still maintain a good performance.
Collapse
|
89
|
Chiang KJ, Nakanishi M, Jung TP. Statistically Optimized Spatial Filtering in Decoding Steady-State Visual Evoked Potentials Based on Task-Related Component Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3070-3073. [PMID: 33018653 DOI: 10.1109/embc44109.2020.9176205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Task-related component analysis (TRCA) has been the most effective spatial filtering method in implementing high-speed brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). TRCA is a data-driven method, in which spatial filters are optimized to maximize inter-trial covariance of time-locked electroencephalographic (EEG) data, formulated as a generalized eigenvalue problem. Although multiple eigenvectors can be obtained by TRCA, the traditional TRCA-based SSVEP detection considered only one that corresponds to the largest eigenvalue to reduce its computational cost. This study proposes using multiple eigen-vectors to classify SSVEPs. Specifically, this study integrates a task consistency test, which statistically identifies whether the component reconstructed by each eigenvector is task-related or not, with the TRCA-based SSVEP detection method. The proposed method was evaluated by using a 12-class SSVEP dataset recorded from 10 subjects. The study results indicated that the task consistency test usually identified and suggested more than one eigenvectors (i.e., spatial filters). Further, the use of additional spatial filters significantly improved the classification accuracy of the TRCA-based SSVEP detection.
Collapse
|
90
|
Zhao D, Li X, Hou X, Feng M, Jiang R. Synchrosqueezing with short-time fourier transform method for trinary frequency shift keying encoded SSVEP. Technol Health Care 2020; 29:505-519. [PMID: 32986635 DOI: 10.3233/thc-202427] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND The frequencies that can evoke strong steady state visual evoked potentials (SSVEP) are limited, which leads to brain-computer interface (BCI) instruction limitation in the current SSVEP-BCI. To solve this problem, the visual stimulus signal modulated by trinary frequency shift keying was introduced. OBJECTIVE The main purpose of this paper is to find a more reliable recognition algorithm for SSVEP-BCI based on trinary frequency shift keying modulated stimuli. METHODS First, the signal modulated by trinary frequency shift keying is simulated by MATLAB. At different noise levels, the empirical mode decomposition, singular value decomposition, and synchrosqueezing with the short-time Fourier transform are used to extract the characteristic frequency and reconstruct the signal. Then, the coherent method is used to demodulate the reconstructed signal. Second, in the paradigm of BCI using trinary frequency shift keying modulated stimuli, the three methods mentioned above are used to reconstruct EEG signals, and canonical correlation analysis and coherent demodulation are used to recognize the BCI instructions. RESULTS For simulated signals, it is found that synchrosqueezing with short-time Fourier transform has a better effect on extracting the characteristic frequencies. For the EEG signal, it is found that the method combining synchrosqueezing with short-time Fourier transform and coherent demodulation has a higher accuracy and information translate rate than other methods. CONCLUSION The method combining synchrosqueezing with short-time Fourier transform and coherent demodulation proposed in this paper can be applied in the SSVEP system based on trinary frequency shift keying modulated stimuli.
Collapse
Affiliation(s)
- Dechun Zhao
- College of bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiaoxiang Li
- School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiaorong Hou
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Mingyang Feng
- College of bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Renping Jiang
- School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| |
Collapse
|
91
|
Wang L, Han D, Qian B, Zhang Z, Zhang Z, Liu Z. The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli. Brain Sci 2020; 10:brainsci10090616. [PMID: 32906625 PMCID: PMC7563221 DOI: 10.3390/brainsci10090616] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/01/2020] [Accepted: 09/03/2020] [Indexed: 01/23/2023] Open
Abstract
Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain-computer interfaces (BCIs). However, whether SSVEP can be used as a reliable indicator has been a controversial issue. We focused on the independence of SSVEP from frequency allocation and number of stimuli. First, a cue-target paradigm was adopted to examine the interaction between SSVEPs evoked by two stimuli with different frequency allocations under different attention conditions. Second, we explored whether signal strength and the performance of SSVEP-based BCIs were affected by the number of stimuli. The results revealed that no significant interaction of SSVEP responses appeared between attended and unattended stimuli under various frequency allocations, regardless of their appearance in the fundamental or second-order harmonic. The amplitude of SSVEP suffered no significant gain or loss under different numbers of stimuli, but the performance of SSVEP-based BCIs varied along with duration of stimuli; that is, the recognition rate was not affected by the number of stimuli when the duration of stimuli was long enough, while the information transfer rate (ITR) presented the opposite trend. It can be concluded that SSVEP is a reliable tool for marking and monitoring multiple stimuli simultaneously in cognitive studies, but much caution should be taken when choosing a suitable duration and the number of stimuli, in order to achieve optimal utility of BCIs in the future.
Collapse
Affiliation(s)
- Lu Wang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China; (L.W.); (D.H.); (B.Q.); (Z.Z.)
| | - Dan Han
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China; (L.W.); (D.H.); (B.Q.); (Z.Z.)
| | - Binbin Qian
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China; (L.W.); (D.H.); (B.Q.); (Z.Z.)
| | - Zhenhao Zhang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China; (L.W.); (D.H.); (B.Q.); (Z.Z.)
| | - Zhijun Zhang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China; (L.W.); (D.H.); (B.Q.); (Z.Z.)
- Correspondence: ; Tel.: +86-571-88273337
| | - Zhifang Liu
- Department of Psychology and Special Education, Hangzhou Normal University, Hangzhou 311121, China;
| |
Collapse
|
92
|
Liang L, Lin J, Yang C, Wang Y, Chen X, Gao S, Gao X. Optimizing a dual-frequency and phase modulation method for SSVEP-based BCIs. J Neural Eng 2020; 17:046026. [PMID: 32726763 DOI: 10.1088/1741-2552/abaa9b] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The design of the stimulation paradigm plays an important role in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) studies. Among various stimulation designs, the dual-frequency paradigm in which two frequencies are used to encode one target is of importance and interest. However, because the number of possible frequency combinations is huge, the existing dual-frequency modulation paradigms failed to optimize the encoding towards the best combinations. Thus, this work aiming at designing a new dual-frequency and phase modulation paradigm with the best combinations stimuli. APPROACH This study proposed a dual-frequency and phase modulation method, which can achieve a large number of targets by making different combinations of two frequencies and an initial phase. This study also designed a set of methods for quickly optimizing the stimulation codes for the dual-frequency and phase modulation method. MAIN RESULTS An online 40-class BCI experiment with 12 subjects obtained an accuracy of 96.06[Formula: see text]4.00% and an averaged information transfer rate (ITR) of 196.09[Formula: see text]15.25 bits min-1, which were much higher than the existing dual-frequency modulation paradigms. Moreover, an offline simulation with a public dataset showed that the optimization method was also effective for optimizing the single-frequency and phase modulation paradigm. SIGNIFICANCE These results demonstrate the high performance of the proposed dual-frequency and phase modulation method and the high efficiency of the optimization method for designing SSVEP stimulation paradigms. In addition, the coding efficiency of the optimized dual-frequency and phase modulation paradigm is higher than that of the single-frequency and phase modulation paradigm, and it is expected to further realize the BCI paradigm with a large amount of targets.
Collapse
Affiliation(s)
- Liyan Liang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | | | | | | | | | | | | |
Collapse
|
93
|
Zhang HY, Stevenson CE, Jung TP, Ko LW. Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1771-1780. [PMID: 32746309 DOI: 10.1109/tnsre.2020.3005771] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Most research in Brain-Computer-Interfaces (BCI) focuses on technologies to improve accuracy and speed. Little has been done on the effects of subject variability, both across individuals and within the same individual, on BCI performance. For example, stress, arousal, motivation, and fatigue can all affect the electroencephalogram (EEG) signals used by a BCI, which in turn impacts performance. Overcoming the impact of such user variability on BCI performance is an impending and inevitable challenge for routine applications of BCIs in the real world. To systematically explore the factors affecting BCI performance, this study embeds a Steady-State Visually Evoked Potential (SSVEP) based BCI into a "game with a purpose" (GWAP) to obtain data over significant lengths of time, under both high- and low-stress conditions. Ten healthy volunteers played a GWAP that resembles popular match-three games, such as Jewel Quest, Zoo Boom, or Candy Crush. We recorded the target search time, target search accuracy, and EEG signals during gameplay to investigate the impacts of stress on EEG signals and BCI performance. We used Canonical Correlation Analysis (CCA) to determine whether the subject had found and attended to the correct target. The experimental results show that SSVEP target-classification accuracy is reduced by stress. We also found a negative correlation between EEG spectra and the SNR of EEG in the frontal and occipital regions during gameplay, with a larger negative correlation for the high-stress conditions. Furthermore, CCA also showed that when the EEG alpha and theta power increased, the search accuracy decreased, and the spectral amplitude drop was more evident under the high-stress situation. These results provide new, valuable insights into research on how to improve the robustness of BCIs in real-world applications.
Collapse
|
94
|
Li Z, Liu K, Deng X, Wang G. Spatial fusion of maximum signal fraction analysis for frequency recognition in SSVEP-based BCI. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102042] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
95
|
Tang J, Xu M, Han J, Liu M, Dai T, Chen S, Ming D. Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling. SENSORS 2020; 20:s20154186. [PMID: 32731432 PMCID: PMC7435370 DOI: 10.3390/s20154186] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/23/2020] [Accepted: 07/25/2020] [Indexed: 02/03/2023]
Abstract
The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.
Collapse
Affiliation(s)
- Jiabei Tang
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
| | - Minpeng Xu
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
| | - Jin Han
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
| | - Miao Liu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
| | - Tingfei Dai
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
| | - Shanguang Chen
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China
| | - Dong Ming
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
- Correspondence:
| |
Collapse
|
96
|
Deng X, Liang Yu Z, Lin C, Gu Z, Li Y. Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation. J Neural Eng 2020; 17:045005. [PMID: 32413885 DOI: 10.1088/1741-2552/ab937e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability. APPROACH In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects' brain control ability online based on quantized attention-gated kernel reinforcement learning. With the output of BSE-NET (confidence score), a shared controller is designed to dynamically adjust the control weight between robot autonomy and human operator. MAIN RESULTS The experimental results show that most of subjects achieved high and stable experimental success rate of approximately 90%. Furthermore, for subjects with different accuracy on EEG decoding, a proper confidence score can be dynamically generated to reflect their levels of brain control ability, and the proposed system can effectively adjust the control weight in all-time shared control. SIGNIFICANCE We discuss how our proposed method shows promise for BCI applications that can evaluate subjects' brain control ability online as well as provide a method for the research on self-adaptive shared control to adaptively balance control weight between subject's instruction and robot autonomy.
Collapse
Affiliation(s)
- Xiaoyan Deng
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China. Pazhou Lab, Guangzhou 510335, People's Republic of China
| | | | | | | | | |
Collapse
|
97
|
Meng J, Xu M, Wang K, Meng Q, Han J, Xiao X, Liu S, Ming D. Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces. SENSORS 2020; 20:s20123588. [PMID: 32630378 PMCID: PMC7348905 DOI: 10.3390/s20123588] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/22/2020] [Accepted: 06/22/2020] [Indexed: 11/16/2022]
Abstract
Brain–computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (0~4 Hz) and energy in high-frequency (20~60 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs.
Collapse
Affiliation(s)
- Jiayuan Meng
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China; (J.M.); (M.X.); (K.W.); (J.H.); (X.X.)
| | - Minpeng Xu
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China; (J.M.); (M.X.); (K.W.); (J.H.); (X.X.)
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300000, China; (Q.M.); (S.L.)
| | - Kun Wang
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China; (J.M.); (M.X.); (K.W.); (J.H.); (X.X.)
| | - Qiangfan Meng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300000, China; (Q.M.); (S.L.)
| | - Jin Han
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China; (J.M.); (M.X.); (K.W.); (J.H.); (X.X.)
| | - Xiaolin Xiao
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China; (J.M.); (M.X.); (K.W.); (J.H.); (X.X.)
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300000, China; (Q.M.); (S.L.)
| | - Dong Ming
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China; (J.M.); (M.X.); (K.W.); (J.H.); (X.X.)
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300000, China; (Q.M.); (S.L.)
- Correspondence:
| |
Collapse
|
98
|
Liu B, Huang X, Wang Y, Chen X, Gao X. BETA: A Large Benchmark Database Toward SSVEP-BCI Application. Front Neurosci 2020; 14:627. [PMID: 32655358 PMCID: PMC7324867 DOI: 10.3389/fnins.2020.00627] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 05/20/2020] [Indexed: 12/31/2022] Open
Abstract
The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases is still limited in this field. Therefore, we present a BEnchmark database Towards BCI Application (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA are in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend using the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database can be downloaded from the following link http://bci.med.tsinghua.edu.cn/download.html.
Collapse
Affiliation(s)
- Bingchuan Liu
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Xiaoshan Huang
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Xiaorong Gao
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| |
Collapse
|
99
|
Tang J, Xu M, Liu Z, Meng J, Chen S, Ming D. A Multifocal SSVEPs-based Brain-Computer Interface with Less Calibration Time .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5975-5978. [PMID: 31947208 DOI: 10.1109/embc.2019.8857450] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
For the past few years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have gotten tremendous progress and attracted increasing attention. To broaden the application of BCIs, researchers have focused on the increasement of the BCI instruction number in recent years. However, with a large number of instructions, the BCI calibration time will be too long to be accepted in practical usage. This study proposed a new coding method based on multifocal steady-state visual evoked potentials (mfSSVEPs), in which 16 targets were binary coded by 4 frequencies. Notably, the training data needed for calibration corresponded to only five out of the sixteen targets. Five volunteers were recruited to test this paradigm. Task-related component analysis combined with a probabilistic model were employed for target recognition. As a result, the accuracy could reach as high as 93.1% with 1s-length data. The highest information transfer rate (ITR) was 101.1 bits/min with an average of 73.9 bits/min. The results indicate that this new paradigm is promising to encode a large BCI instruction set with less trainings.
Collapse
|
100
|
Shafiul Hasan SM, Siddiquee MR, Atri R, Ramon R, Marquez JS, Bai O. Prediction of gait intention from pre-movement EEG signals: a feasibility study. J Neuroeng Rehabil 2020; 17:50. [PMID: 32299460 PMCID: PMC7164221 DOI: 10.1186/s12984-020-00675-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 04/01/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. METHODS An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. RESULTS Using a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min. CONCLUSION Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.
Collapse
Affiliation(s)
- S. M. Shafiul Hasan
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Masudur R. Siddiquee
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Roozbeh Atri
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Rodrigo Ramon
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - J. Sebastian Marquez
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Ou Bai
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| |
Collapse
|