Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification

Islam, Nahidul; Sulaiman, Norizam; Rashid, Mamunur; Bari, Bifta Sama; Hasan, Jahid; Mustafa, Mahfuzah; Jadin, Mohd Shawal

doi:10.1109/icset51301.2020.9265370

Cited by 5 publications

(2 citation statements)

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“…Several studies have reported that AR and PSD were extracted features and then classified using SVM in the Graz dataset B, known as a dataset for motor imagery classification [37,38]. Moreover, an identification accuracy of up to 95.89% was achieved between the imaginary motions of the tongue and the left little finger for an individual in the FFT-based MI data using SVM [40]. Lee et al have reported that the time domain parameter (TDP) outperformed CSP in identification accuracy between hand grasping and wrist twisting, based on the MI tasks using SVM [41]; this phenomenon showed that TDP (93.6%) was more accurate than CSP (91.4%).…”

Section: Introductionmentioning

confidence: 99%

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Bak

Jeong

2023

IEEE Access

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Human brain activities-electroencephalogram (EEG) signals-are likely to provide a secure biometric approach for user identification because they are more sensitive, secretive, and difficult to replicate. Many studies have recently focused on identifying and quantifying important frequency patterns in motor imagery (MI), recorded through EEG. However, there is still a lack of an optimal methodology for recognizing users with EEG-based MI. Therefore, we aimed to propose an EEG-MI methodology that utilizes optimized feature extraction methods and classifiers to improve user-aware accuracy. To accomplish this goal, we extracted four features related to MI and compared the accuracies for recognizing users using a support vector machine (SVM) and Gaussian Naïve Bayes (GNB). We then used the half-total error rate (HTER) to determine whether the results were reliable due to an imbalance problem caused by the differences in the data sizes. Thus, we used a common spatial pattern (CSP) to achieve the highest user identification accuracies of 98.97% and 97.47% using SVM and GNB, respectively. All user recognition accuracies are guaranteed by the HTERs, which are below 0.5. However, CSP has the disadvantage of decreasing accuracy on a small dataset scale. Therefore, we proposed and tested a statistical methodology for estimating a minimum dataset scale to ensure CSP performance. We confirm that the used dataset adequately guarantees CSP performance. This study makes a great contribution to the field of information security by presenting an EEG-MI methodology that improves the identification accuracy in human biometrics based on EEG-MI signals.

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Section: Introductionmentioning

confidence: 99%

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Bak

Jeong

2023

IEEE Access

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“…The direct transfer of information between the human brain and the environment are known as brain-computer interfaces (BCIs) [1]. In other words, BCIs evaluate changes in the brain's electrical activity in response to external stimuli or user intents and transform them directly into output commands for controlling a device or application.…”

Section: Introductionmentioning

confidence: 99%

A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

et al. 2021

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The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology.

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