A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain's sensorimotor region activated tasks

Wei, Minghua; Lin, Feng

doi:10.1108/ijicc-02-2020-0019

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…However, such individual and session-specific templates will cause a lot of computational consume, which leads to a low practical efficiency for applications. To solve the influence of covariate shift problem, a lot of researches based on transfer learning have been successfully applied to motor imagery-based BCI and achieved excellent results (Wei and Lin, 2020; Li, 2020). In recent years, some researchers have also carried out the exploration research studies for cross-subject SSVEP-BCI with transfer learning (Wang et al , 2021).…”

Section: Ablation Study and Results Discussionmentioning

confidence: 99%

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Luo

2022

IJICC

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PurposeSteady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approachTo survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.FindingsFor the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/valueBased on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

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Section: Ablation Study and Results Discussionmentioning

confidence: 99%

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Luo

2022

IJICC

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“…The proposed method achieved 76.62% accuracy with a kappa of 0.69 for the classification four-class MI task on DS5, encouraging the problem of inter-and intra-class variations. Wei and Lin [17] sought to solve the problems of poor performance, low efficiency and weak robustness using a multi-dimensional fusion features-based classification. They used an improved Morlet wavelet algorithm to extract stable features from the frequency spectrum, especially for nonlinear and nonstationary signals.…”

Section: Wavelet-based Methodsmentioning

confidence: 99%

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

Yilmaz,

Yilmaz

2023

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Non-invasive acquisition and analysis of human brain signals play a crucial role in the development of brain-computer interfaces, enabling their widespread applicability in daily life. Motor imagery has emerged as a prominent technique for the advancement of such interfaces. While initial machine and deep learning studies have shown promising results in the context of motor imagery, several challenges remain to be addressed prior to their extensive adoption. Deep learning, renowned for its automated feature extraction and classification capabilities, has been successfully employed in various domains. Notably, recent research efforts have focused on processing and classifying motor imagery EEG signals using two-dimensional data formats, yielding noteworthy advancements. Although existing literature encompasses reviews primarily centered on machine learning or deep learning techniques, this paper uniquely emphasizes the review of methods for constructing two-dimensional image features, marking the first comprehensive exploration of this subject. In this study, we present an overview of datasets, survey a range of signal-to-image conversion methods, and discuss classification approaches. Furthermore, we comprehensively examine the current challenges and outline future directions for this research domain.

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“…In order to provide outcomes that are understandable and advance our knowledge of how the network learns from input representation sets, the idea of intermediate feature visualizations has been investigated. 69,70 Similar to that, correlation maps 71,72 and saliency maps 73 are used to create visualizations. Another approach for interpretability is to add interpretable layers to the network architecture.…”

Section: Limitations and Areas For Future Researchmentioning

confidence: 99%

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG

Chugh,

Aggarwal,

Balyan

2023

Clin EEG Neurosci

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Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.

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A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain's sensorimotor region activated tasks

Cited by 12 publications

References 38 publications

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG

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