EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction

Komijani, Hossein; Parsaei, Mohammad Reza; Khajeh, Ebrahim; Golkar, Mohammad Javad; Zarrabi, Houman

doi:10.1007/s00521-017-3213-3

Cited by 14 publications

(9 citation statements)

References 37 publications

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“…For two class problems, the highest accuracy results for using 1 − 2 s of data were reported in [4], for using 2 − 4 s of data in [5,6,7,8,9], and for using equal or greater than 4 s of data in [10]. For multi-class problems, the highest accuracy results were achieved in [11] for using 1 − 2 s of data, in [9,12,13] for using 2 − 4 s of data, and in [14,15,16] for using equal or greater than 4 s of data. Considering these studies, it can be concluded that with respect to the required buffering lag, the decoding process has been relatively slow.…”

Section: Introductionmentioning

confidence: 99%

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

Shamsi¹,

Haddad²,

Najafizadeh³

2021

J. Neural Eng.

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Objective. Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem. Approach. The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification. Main results. Features extracted from various durations of poststimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation. Significance. Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms).This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

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Section: Introductionmentioning

confidence: 99%

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

Shamsi¹,

Haddad²,

Najafizadeh³

2021

J. Neural Eng.

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“…Yang et al designed a classification method called adaptive kernel fisher support vector machine (KF-SVM) is designed and applied to EEG MI classification in BCI [60]. Komijani et al presents MI classification for BCI systems using a recurrent adaptive neuro-fuzzy interface system (ANFIS), and the classification system is based on time-series prediction [61]. Lahiri et al proposed to use the whole classifier composed of the k-nearest neighbor (KNN) layer for classification [62].…”

Section: Different Classifiers In Motor Imagerymentioning

confidence: 99%

An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Wang

Tang

et al. 2020

IEEE Access

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Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection. INDEX TERMS Electroencephalogram, motor imagery (MI), filter bank common spatial pattern (FBCSP), spiking neural networks (SNN). I. INTRODUCTION Electroencephalography (EEG) signal is usually used in brain-computer interface (BCI) systems due to its high temporal resolution [1]. Motor imagery (MI)-based BCI is one of classical paradigms and has been employed to restore the communication pathway or movement function for disabled, paralyzed, and stroke patients [2], [3]. Patients are able to The associate editor coordinating the review of this manuscript and approving it for publication was Li He .

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“…In this paper we address the problem of using motor activity records of schizophrenic individuals and a control group to predict whether a subject has been diagnosed with schizophrenia. A core challenge to obtain this is on how to extract useful features from the activity data that are able to efficiently separate schizophrenia patients from healthy controls, and have been addressed in several studies [13,14,15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of schizophrenia from activity data using hidden Markov model parameters

Boeker

Hammer

Riegler

et al. 2022

Neural Comput & Applic

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In this paper, we address the problem of predicting schizophrenia based on a persons measured motor activity over time. A key challenge to achieve this is how to extract features from the activity data that can efficiently separate schizophrenia patients from healthy subjects. To achieve this, we suggest to fit time dependent hidden Markov models with and without integrated covariates and letting the estimated model parameters represent our features. To further evaluate the efficiency of these features, we suggest to use them as features in a classification method (logistic regression) to separate schizophrenia patients from healthy subjects. The results show that the estimated hidden Markov model parameters are well-performing in predicting schizophrenia, and outperform features derived from other methods in the literature in terms of goodness-of-fit and classification performance.

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EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction

Cited by 14 publications

References 37 publications

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Prediction of schizophrenia from activity data using hidden Markov model parameters

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