Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Rashid, Nasir; Iqbal, Javaid; Javed, Amna; Tiwana, Mohsin I.; Khan, Umar Shahbaz

doi:10.1155/2018/2695106

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…The increasing awareness of brain–computer interfaces (BCI) for brain signal analysis has sparked new interest in electroencephalogram (EEG) acquisition device development. Various rehabilitation [ 1 ], entertainment, and even security [ 2 ] applications can be implemented by post-processing [ 3 , 4 , 5 ] such electrical signals recorded from the human scalp. However, developing a BCI is a challenging task due to the noisy and variable nature of the EEG signal itself.…”

Section: Introductionmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas

2018

Sensors

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The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

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Section: Introductionmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas

2018

Sensors

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“…Rashid et al used alpha and beta waves of the EEG for the control of upper limb prostheses [ 124 ]. EEG signals were taken by the Emotiv headset with 14 electrodes and a sampling frequency of 128 Hz during defined finger movements.…”

Section: Eeg Measurementsmentioning

confidence: 99%

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.

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“…This paper compares different pattern recognition methods by evaluating different classifiers. There are four classifiers involved in the experiments i.e., support vector machine (SVM) [13], k-nearest neighborhood (kNN) [14], random forest (RF) [15], and linear discriminant analysis [9]. As for SVM, the radial basis function (RBF) kernel with C = 1.0 dan gamma = 0.2.…”

Section: E Classificationmentioning

confidence: 99%

“…In addition, there are several classifiers have been tested and employed for EEG pattern recognition. Among them are support vector machine (SVM) [13], k-nearest neighborhood (kNN) [14], random forest (RF) [15], and linear discriminant analysis [9] Unfortunately, most of the EEG pattern recognition on the limb movements focused on a binary classification that classifies either foot and hand movements, or right and lefthand movements [16]. Luckily, the trend has extended to finger movement [8][9][10] [17].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Anam¹,

Nuh²,

Al-Jumaily³

2019

EECSI

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The detection of a hand movement beforehand can be a beneficent tool to control a prosthetic hand for upper extremity rehabilitation. To be able to achieve smooth control, the intention detection is acquired from the human body, especially from brain signal or electroencephalogram (EEG) signal. However, many constraints hamper the development of this brain-computer interface (BCI), especially for finger movement detection. Most of the researchers have focused on the detection of the left and right-hand movement. This article presents the comparison of various pattern recognition method for recognizing five individual finger movements, i.e., the thumb, index, middle, ring, and pinky finger movements. The EEG pattern recognition utilized common spatial pattern (CSP) for feature extraction. As for the classifier, four classifiers, i.e., random forest (RF), support vector machine (SVM), k-nearest neighborhood (kNN), and linear discriminant analysis (LDA) were tested and compared to each other. The experimental results indicated that the EEG pattern recognition with RF achieved the best accuracy of about 54%. Other published publication reported that the classification of the individual finger movement is still challenging and need more efforts to achieve better performance.

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Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Cited by 15 publications

References 22 publications

Development of a Modular Board for EEG Signal Acquisition

Development of a Modular Board for EEG Signal Acquisition

Measuring Biosignals with Single Circuit Boards

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

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