Multi-ganglion ANN based feature learning with application to P300-BCI signal classification

Gao, Wei; Guan, Jiunian; Gao, Junfeng; Zhou, Dao

doi:10.1016/j.bspc.2014.12.007

Cited by 29 publications

(12 citation statements)

References 34 publications

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“…Sensitivity and specificity for up-and downward and left-and rightward movements in the proposed method are calculated based on the relations (15) and (16), as seen in Table 4. In this study, the K-LSVM method was proposed based on known special patterns.…”

Section: Resultsmentioning

confidence: 99%

“…After obtaining satisfactory results, the attention of researchers was drawn to the multi-dimensional cursor control with the aim of increasing the relationship between the user and machine. Based on the studies, the multi-dimensional cursor can be controlled by implementing different brain signals, for example the P300 potential [15,16], synchronous and asynchronous signals [17] and evoked potentials [18,19]. They showed well that the input signals of an EEG-based brain-computer-interface system have commonly weak, non-constant and mind tasks-related noises with various artifacts such as external electromagnetic waves and electromyogram and electro-echogram waves.…”

Section: Introductionmentioning

confidence: 99%

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Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

Mohammadi

Einalou

Hosseinzadeh

et al. 2020

Preprint

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In this study, we present the detection of the up- and downward as well as the right- and leftward motion of cursor based on feature extraction. Feature Extraction and selection for finding the proper classifier among the data mining methods are of great importance. In the proposed method, the hybrid K-means clustering algorithm and the linear support vector machine (LSVM) classifier have been used for extracting the important features and detecting the cursor motion. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. The database of the Karadeniz Technical University of Turkey has been used in the present article. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up- and downward and left- and rightward movements in each person to increase by 2–10%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

Mohammadi

Einalou

Hosseinzadeh

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The significant features for classification are extracted either by eliminating irrelevant and redundant data points or by identifying and selecting data points containing critical information. In most of the literature we have surveyed, epoch averaging (with or without PCA/ICA) was used for extracting features, while in some works discrete wavelet transform (DWT), 47,[49][50][51][52][53] channel correlation analysis (CCA), 54 artificial neural networks (ANNs), 55 and phase synchronization 56 were used.…”

Section: Process Flowmentioning

confidence: 99%

“…Gu et al 85 have applied least-squares SVM to develop a semisupervised VP3S, which was able to attain 85% online classification accuracy through training and learning process of duration less than 3.5 minutes. Gao et al 55 have combined feature extraction using artificial neural network (5 ganglion) and classification based on SVM with radial basis function (RBF) kernel to obtain a VP3S, which was reported to outperform the traditional SVM with PCA scheme for VP3S.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms

Philip

George

2019

Clin EEG Neurosci

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Brain-computer interfaces are sophisticated signal processing systems, which directly operate on neuronal signals to identify specific human intents. These systems can be applied to overcome certain disabilities or to enhance the natural capabilities of human beings. The visual P300 mind-speller is a prominent one among them, which has opened up tremendous possibilities in movement and communication applications. Today, there exist many state-of-the-art visual P300 mind-speller implementations in the literature as a result of numerous researches in this domain over the past 2 decades. Each of these systems can be evaluated in terms of performance metrics like classification accuracy, information transfer rate, and processing time. Various classification techniques associated with these systems, which include but are not limited to discriminant analysis, support vector machine, neural network, distance-based and ensemble of classifiers, have major roles in determining the overall system performances. The significance of a proper review on the recent developments in visual P300 mind-spellers with proper emphasis on their classification algorithms is the key insight for this work. This article is organized with a brief introduction to P300, concepts of visual P300 mind-spellers, the survey of literature with special focus on classification algorithms, followed by the discussion of various challenges and future directions.

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“…After obtaining satisfactory results, the attention of researchers was drawn to the multidimensional cursor control with the aim of increasing the relationship between the user and machine. Based on the studies, the multi-dimensional cursor can be controlled by implementing different brain signals, for example the P300 potential [15,16], synchronous and asynchronous signals [17] and evoked potentials [18,19]. They showed well that the input signals of an EEG-based brain-computer-interface system have commonly weak, non-constant and mind tasks-related noises with various artifacts such as external electromagnetic waves and electromyogram and electro-echogram waves.…”

mentioning

confidence: 99%

Cursor movement detection in brain-computer-interface systems using the K-means clustering method and LSVM

et al. 2021

View full text Add to dashboard Cite

In this study, we present the detection of the up-downward as well as the right- leftward motion of cursor based on feature extraction. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up-downward and left- rightward movements in each person to increase by 2–10 %.

show abstract

Multi-ganglion ANN based feature learning with application to P300-BCI signal classification

Cited by 29 publications

References 34 publications

Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms

Cursor movement detection in brain-computer-interface systems using the K-means clustering method and LSVM

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