Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis

Lange, Gerrit; Low, Cheng Yee; Johar, Khairunnisa; Hanapiah, Fazah Akthar; Kamaruzaman, Fadhlan

doi:10.1016/j.protcy.2016.08.048

Cited by 39 publications

(21 citation statements)

References 17 publications

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“…This result indicates that similar and dissimilar faces in fact shares some non-discriminatively low-level features that may contribute to misclassification of them as belong to similar faces. Carefully selecting those features and ranking them or even removing them might produce better results for this algorithm, such as using Linear Discriminant Analysis (LDA) [24]. However, for the purpose of AVRS development, it requires faster algorithm such as proposed, and thus we chose D = 7 for the development of the system and we omit any feature ranking or feature discrimination methods.…”

Section: B Results On Face Similarity Detectionmentioning

confidence: 99%

Automated Visitor Record System

Zaman

Sulaiman

Abdullah

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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As normally practiced, the information of visitor is recorded manually by either the visitor or the guard when the visitor arrives at the guard house. This manual process takes large amount of time and involves tedious works. Furthermore, it is prone to fraudulent information provided by visitors and occasionally interferes with guard's actual job in securing the area. This paper aims to provide an automated solution to these aforementioned problems. We address these problems by replacing the existing manual way of recording visitor information by an automated and fully computerized system. The solution proposed uses Radio-Frequency Identification (RFID), smart card information retrieval as well as computer vision and image processing to record and manage visitors' data. To evaluate the similarity between face images from camera and National Registration Identification Card (NRIC), we propose a method to find dissimilarity index between the faces. We found that the system minimizes the need for human interventions, improves the time required during recording of visitors' information as well as efficiently manages and analyses visitors' records. Additionally, our proposed method for finding face similarity yields a promising result of TPR = 0.914 and FPR = 0.140 when tested using publicly available face dataset called AR Dataset. The system is able to minimize the need for guard and visitor interventions, improves the time required during recording of visitors' information especially for recurring visits and also capable of securely and efficiently manages and analyses visitors' records.

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Section: B Results On Face Similarity Detectionmentioning

confidence: 99%

Automated Visitor Record System

Zaman

Sulaiman

Abdullah

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…CAS Filho et al used the graph method to classify human hands signals, achieving a high recognition rate of up to 98% [ 49 ]. Research shows that the recognition of motor information in brain signals using the BCI is an effective method [ 50 , 51 , 52 ]. Although it is very accurate to identify motion characteristics using EEG, the authors cannot confirm that it also has an efficient motion recognition rate when the auxiliary robot is controlled in real time outside the field of vision.…”

Section: Discussionmentioning

confidence: 99%

Study of the Home-Auxiliary Robot Based on BCI

Wang

Zhang

et al. 2018

Sensors

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A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person’s field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects’ electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89.

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“…Based on the average accuracies on Tables 1 -3, Mu and Beta waves calculated using the CSP algorithm as the features showed a good result in helping to separate the 2-class imaginary movement. In a previous study, CSP also demonstrated high accuracies (above 73 %) for calculating the features (EMG based marker) in distinguishing grasping and release movement combined with the Linear Discriminant Analysis (LDA) as a classifier [19]. The previous study used real movements as features, while this study tried to use imaginary movements to generate the signal like a post-stroke patient who could not move their hand freely.…”

Section: Classifying Imaginary Hand Movement Through Eeg Signalmentioning

confidence: 95%

Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation

Rahma

Hendradi

Ama

2018

Walailak J Sci & Tech

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Brain-Computer-Interface (BCI) has been widely used in the field of neuro-rehabilitation such as automatic controls based on brain commands to upper and lower extremity prosthesis devices in patients with paralysis. In a post-stroke period, approximately 50 % of stroke survivors have unilateral motor deficits leading to a sustained decline in chronic upper extremity function. Stroke affects patients in their productive and elderly age which is potentially creating new problems in national health development. BCI could be used to aid post-stroke patient recovery, so the motion detection and classification are essential for optimizing the BCI device control. Therefore, this study aims to distinguish several hand functions such as grasping, pinching, and hand lifting from releasing movement by the standard actions performed during post-stroke rehabilitation based on brain signals of a healthy subject obtained from an electroencephalogram (EEG) with a 5 channels electrode. In this study, the EEG signals were decomposed using a Discrete Wavelet Transform (DWT) then filtered by a bandpass filter to generate Mu and Beta waves, which were correlated with imaginary movement. Then, the Mu and Beta waves were calculated using a Common Spatial Pattern (CSP) algorithm as the inputs for Extreme Learning Machine (ELM) to distinguish 2 types of imaginary hand movements (grasping v. releasing, pinching v. releasing, hand lifting v. releasing). The results of these classifications shown that ELM and CSP were useful features in distinguishing 2 types of motion with software/system accuracy average above 95 %. Therefore, this could be useful for optimizing BCI devices in neuro-rehabilitation, moreover by combining it with a Functional Electrical Stimulator (FES) as a self-therapy for post-stroke patients.

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Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis

Cited by 39 publications

References 17 publications

Automated Visitor Record System

Automated Visitor Record System

Study of the Home-Auxiliary Robot Based on BCI

Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation

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