EEG signals classification and determination of optimal feature-classifier combination for predicting the movement intent of lower limb

Malik, Anjum Naeem; Iqbal, Javaid; Tiwana, Mohsin I.

doi:10.1109/icrai.2016.7791226

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…In order to verify the difference between normal motor coordination imagery involving the lower limbs and that of poststroke hemiplegia gait imagery, the time-domain MRCP feature, power spectrum feature, time-frequency feature, network attribute feature, combined features of network attributes, and the adjacency matrix space were extracted. Since the SVM classifier has a good classification effect on small sample data [ 57 ], SVM was used for classification verification.…”

Section: Methodsmentioning

confidence: 99%

Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG‐Based Brain Network

Zhou

Gong

et al. 2021

Computational Intelligence and Neuroscience

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Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Time-domain analysis, power spectrum analysis, time-frequency analysis, brain network analysis, and statistical analysis were used to explore the neural mechanisms of motor coordination imagery involving the lower limbs. Then, EEG-based brain network features were extracted, and a support vector machine was used for decoding. The results showed that the two employed motor coordination imageries mainly activated sensorimotor areas; the frequency band power was mainly concentrated within theta and alpha bands, and brain functional connections mainly occurred in the right forehead. The combination of the network attributes of the EEG-based brain network and the spatial features of the adjacency matrix had good separability for the two kinds of gait imagery ( p < 0.05), and the average classification accuracy of the combination feature was 92.96% ± 7.54%. Taken together, our findings suggest that brain network features can be used to identify normal gait imagery and hemiplegic gait imagery after stroke.

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Section: Methodsmentioning

confidence: 99%

Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG‐Based Brain Network

Zhou

Gong

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…EEG, EMG, fMRI, fNIRS, ECoG are among the most widely utilized biological signals in BCI systems to observe brain neurophysiological activities [3]. Among these signals, the EEG signal is the most popular one as it has more portable recording hardware and the ability to reflect neuroelectrical stimulation of the brain in actions [4][5][6][7][8][9][10][11]. Furthermore, the EEG is an essential estimation of the brain's alertness, death, coma, locate the damaged areas, control the anesthesia depth, investigate and monitor epilepsy, and diagnose many other neurological disorders.…”

Section: Introductionmentioning

confidence: 99%

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

Sunny¹,

Hossain²,

Afroze³

et al. 2021

Biomed. Phys. Eng. Express

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Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

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Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects

et al. 2022

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EEG signals classification and determination of optimal feature-classifier combination for predicting the movement intent of lower limb

Cited by 6 publications

References 19 publications

Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG‐Based Brain Network

Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG‐Based Brain Network

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects

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