1
|
Zheng X, Zhang X, Xu G, Zhang R. Enhancing Performance of Single-Channel SSVEP-Based Visual Acuity Assessment via Mode Decomposition. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4203-4210. [PMID: 37812551 DOI: 10.1109/tnsre.2023.3323000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
This study aimed to improve the performance of single-channel steady-state visual evoked potential (SSVEP)-based visual acuity assessment by mode decomposition methods. Using the SSVEP dataset induced by the vertical sinusoidal gratings at six spatial frequency steps from 11 subjects, 3-40-Hz band-pass filtering and other four mode decomposition methods, i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and variational mode decomposition (VMD), were used to preprocess the single-channel SSVEP signals from Oz electrode. After comparing the SSVEP signal characteristics corresponding to each mode decomposition method, the visual acuity threshold estimation criterion was used to obtain the final visual acuity results. The agreement between subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for band-pass filtering (-0.095 logMAR), EMD (-0.112 logMAR), EEMD (-0.098 logMAR), ICEEMDAN (-0.093 logMAR), and VMD (-0.090 logMAR) was all pretty good, with an acceptable difference between FrACT and SSVEP acuity for band-pass filtering (0.129 logMAR), EMD (0.083 logMAR), EEMD (0.120 logMAR), ICEEMDAN (0.103 logMAR), and VMD (0.108 logMAR), finding that the visual acuity obtained by these four mode decompositions had a lower limit of agreement and a lower or close difference compared to the traditional band-pass filtering method. This study proved that the mode decomposition methods can enhance the performance of single-channel SSVEP-based visual acuity assessment, and also recommended ICEEEMDAN as the mode decomposition method for single-channel electroencephalography (EEG) signal denoising in the SSVEP visual acuity assessment.
Collapse
|
2
|
Niu L, Bin J, kong shuai Wang J, Zhan G, Zhang L, Gan Z, Kang X. A dynamically optimized time-window length for SSVEP based hybrid BCI-VR system. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
|
3
|
Albahri AS, Al-qaysi ZT, Alzubaidi L, Alnoor A, Albahri OS, Alamoodi AH, Bakar AA. A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology. Int J Telemed Appl 2023; 2023:7741735. [PMID: 37168809 PMCID: PMC10164869 DOI: 10.1155/2023/7741735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/01/2023] [Accepted: 03/16/2023] [Indexed: 05/13/2023] Open
Abstract
The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.
Collapse
Affiliation(s)
- A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Z. T. Al-qaysi
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | | | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | | |
Collapse
|
4
|
Bibliometric analysis on Brain-computer interfaces in a 30-year period. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04226-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
|
5
|
Neghabi M, Marateb HR, Mahnam A. Novel frequency-based approach for detection of steady-state visual evoked potentials for realization of practical brain computer interfaces. BRAIN-COMPUTER INTERFACES 2022. [DOI: 10.1080/2326263x.2022.2050513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mehrnoosh Neghabi
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Amin Mahnam
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
| |
Collapse
|
6
|
Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach. SENSORS 2021; 21:s21165308. [PMID: 34450750 PMCID: PMC8439358 DOI: 10.3390/s21165308] [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: 06/07/2021] [Revised: 07/31/2021] [Accepted: 08/01/2021] [Indexed: 01/16/2023]
Abstract
This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.
Collapse
|
7
|
Hong J, Qin X. Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Over past two decades, steady-state evoked potentials (SSVEP)-based brain computer interface (BCI) systems have been extensively developed. As we all know, signal processing algorithms play an important role in this BCI. However, there is no comprehensive review of the latest development of signal processing algorithms for SSVEP-based BCI. By analyzing the papers published in authoritative journals in nearly five years, signal processing algorithms of preprocessing, feature extraction and classification modules are discussed in detail. In addition, other aspects existed in this BCI are mentioned. The following key problems are solved. (1) In recent years, which signal processing algorithms are frequently used in each module? (2) Which signal processing algorithms attract more attention in recent years? (3) Which modules are the key to signal processing in BCI field? This information is very important for choosing the appropriate algorithms, and can also be considered as a reference for further research. Simultaneously, we hope that this work can provide relevant BCI researchers with valuable information about the latest trends of signal processing algorithms for SSVEP-based BCI systems.
Collapse
Affiliation(s)
- Jie Hong
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Xiansheng Qin
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| |
Collapse
|
8
|
Alharbi N. Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis. ACTA ACUST UNITED AC 2021; 2:e21044. [PMID: 34076627 PMCID: PMC8078444 DOI: 10.2196/21044] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 12/26/2020] [Accepted: 02/14/2021] [Indexed: 01/23/2023]
Abstract
Background Infectious disease is one of the main issues that threatens human health worldwide. The 2019 outbreak of the new coronavirus SARS-CoV-2, which causes the disease COVID-19, has become a serious global pandemic. Many attempts have been made to forecast the spread of the disease using various methods, including time series models. Among the attempts to model the pandemic, to the best of our knowledge, no studies have used the singular spectrum analysis (SSA) technique to forecast confirmed cases. Objective The primary objective of this paper is to construct a reliable, robust, and interpretable model for describing, decomposing, and forecasting the number of confirmed cases of COVID-19 and predicting the peak of the pandemic in Saudi Arabia. Methods A modified singular spectrum analysis (SSA) approach was applied for the analysis of the COVID-19 pandemic in Saudi Arabia. We proposed this approach and developed it in our previous studies regarding the separability and grouping steps in SSA, which play important roles in reconstruction and forecasting. The modified SSA approach mainly enables us to identify the number of interpretable components required for separability, signal extraction, and noise reduction. The approach was examined using different levels of simulated and real data with different structures and signal-to-noise ratios. In this study, we examined the capability of the approach to analyze COVID-19 data. We then used vector SSA to predict new data points and the peak of the pandemic in Saudi Arabia. Results In the first stage, the confirmed daily cases on the first 42 days (March 02 to April 12, 2020) were used and analyzed to identify the value of the number of required eigenvalues (r) for separability between noise and signal. After obtaining the value of r, which was 2, and extracting the signals, vector SSA was used to predict and determine the pandemic peak. In the second stage, we updated the data and included 81 daily case values. We used the same window length and number of eigenvalues for reconstruction and forecasting of the points 90 days ahead. The results of both forecasting scenarios indicated that the peak would occur around the end of May or June 2020 and that the crisis would end between the end of June and the middle of August 2020, with a total number of infected people of approximately 330,000. Conclusions Our results confirm the impressive performance of modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from a noisy time series and then making a reliable prediction of daily confirmed cases using the vector SSA method.
Collapse
Affiliation(s)
- Nader Alharbi
- King Saud bin Abdulaziz University for Health Sciences King Abdullah International Medical Research Center Riyadh Saudi Arabia
| |
Collapse
|
9
|
A hybrid method for muscle artifact removal from EEG signals. J Neurosci Methods 2021; 353:109104. [PMID: 33617916 DOI: 10.1016/j.jneumeth.2021.109104] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/10/2021] [Accepted: 02/13/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND Electroencephalogram (EEG) signals may be contaminated with muscle artifacts that are usually difficult to be removed. NEW METHOD In this article, a new hybrid method for suppressing muscle artifacts is proposed. Our method leverages variational mode decomposition (VMD) and canonical correlation analysis (CCA) algorithms. Each channel of EEG is decomposed into intrinsic mode functions (IMFs) with VMD to achieve an extended data set that contains more channels than the original data set. The potential artifact components are decomposed by CCA for further isolation. RESULTS The proposed method is evaluated with semi-simulation and real contaminated EEG signals. The results show that the performance of removing artifacts for VMD-CCA exceeds the comparison methods. COMPARISON WITH EXISTING METHODS Regardless of the number of EEG channels and the signal-to-noise ratio of the signal, the VMD-CCA approach is superior to the existing methods. As the number of EEG channels decreases, the average de-artifact effects of VMD-CCA and the comparison approaches are basically the same, but the randomness increases. CONCLUSIONS The VMD-CCA method can effectively isolate muscle artifacts in EEG in case of multiple channels or few channels.
Collapse
|
10
|
Katyal EA, Singla R. EEG-based hybrid QWERTY mental speller with high information transfer rate. Med Biol Eng Comput 2021; 59:633-661. [PMID: 33594631 DOI: 10.1007/s11517-020-02310-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 12/30/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller. METHODS The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy. RESULTS On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F). COMPARISON The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller. CONCLUSION QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.
Collapse
Affiliation(s)
- Er Akshay Katyal
- ICE Department, Dr B.R. Ambedkar N.I.T. Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144011, India.
| | - Rajesh Singla
- ICE Department, Dr B.R. Ambedkar N.I.T. Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144011, India
| |
Collapse
|
11
|
Katyal A, Singla R. Synchronized Detection of Evoked Potentials to Drive a High Information Transfer Rate Hybrid Brain-Computer Interface Application. ADVANCED BIOMEDICAL ENGINEERING 2021. [DOI: 10.14326/abe.10.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Akshay Katyal
- Department of Instrumentation and Control Engineering, Dr BR Ambedkar National Institute of Technology Jalandhar
| | - Rajesh Singla
- Department of Instrumentation and Control Engineering, Dr BR Ambedkar National Institute of Technology Jalandhar
| |
Collapse
|
12
|
Kalantari M. Forecasting COVID-19 pandemic using optimal singular spectrum analysis. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110547. [PMID: 33311861 PMCID: PMC7719007 DOI: 10.1016/j.chaos.2020.110547] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/12/2020] [Accepted: 12/04/2020] [Indexed: 05/17/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.
Collapse
Affiliation(s)
- Mahdi Kalantari
- Department of Statistics, Payame Noor University, 19395-4697, Tehran, Iran
| |
Collapse
|
13
|
Katyal A, Singla R. A novel hybrid paradigm based on steady state visually evoked potential & P300 to enhance information transfer rate. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101884] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
14
|
Cirillo MD, d’Affonsêca Netto A, Toffolo GM, Miranda de Sá AMFL. Development of a brain computer interface based on steady-state visual evoked potential with multiple intermittent photo stimulation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
15
|
Abstract
Singular spectrum analysis (SSA) is a non-parametric forecasting and filtering method that has many applications in a variety of fields such as signal processing, economics and time series analysis. One of the four steps of the SSA, which is called the grouping step, plays a pivotal role in the SSA because reconstruction and forecasting of results are directly affected by the outputs of this step. Usually, the grouping step of SSA is time consuming as the interpretable components are manually selected. An alternative more optimized approach is to apply automatic grouping methods. In this paper, a new dissimilarity measure between two components of a time series that is based on various matrix norms is first proposed. Then, using the new dissimilarity matrices, the capabilities of different hierarchical clustering linkages are compared to identify appropriate groups in the SSA grouping step. The performance of the proposed approach is assessed using the corrected Rand index as validation criterion and utilizing various real-world and simulated time series.
Collapse
|