EEG-based brain controlled prosthetic arm

Bright, Dany; Nair, Amrita; Salvekar, Devashish; Bhisikar, Swati

doi:10.1109/casp.2016.7746219

Cited by 83 publications

(37 citation statements)

References 6 publications

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“…In a BCI system, specific patterns of brain activity are translated into control commands in the purpose of particular devices operation [2]. Mind-controlled wheelchair [3], home appliances [4], prosthetic arm controlling [5], spelling system [6], emotion detection system [7] and biometrics [8] are the popular BCI applications [9]. Currently, BCI applications have been widened from medical to non-medical fields, for example, BCI based games and virtual reality [9].…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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“…Implanted EMG sensors are a promising technology that can provide high-quality signals; however, they are costly, invasive, and only in the primary stages of human use [33]. Myoelectric control also relies on undamaged neuromuscular pathways being accessible to present the EMG signal [7]. Although strides are being taken in targeted muscle reinnervation (TMR) to allow for greater EMG accessibility [21], this may not be possible in all cases.…”

Section: Introductionmentioning

confidence: 99%

“…Brain signals have been successfully detected through the use of an EEG to effectively control prosthetics including that of bionic arms. Current bionic hands have been able to demonstrate two degrees of freedom in the fingers [4,7]. Additionally, Vidaurre et al demonstrated EEG-based control of an exoskeleton [38].…”

Section: Introductionmentioning

confidence: 99%

“…This arm is Biomimetic Prostheticscontrolled through a variety of methods including EMG electrodes and features 10 degrees of freedom. A few drawbacks to muscle-based control technology, however, are its generally high cost [7] as well as repeatability and reliability issues in surface EMG signals [36], making this input method difficult to use in prosthetic control. Implanted EMG sensors are a promising technology that can provide high-quality signals; however, they are costly, invasive, and only in the primary stages of human use [33].…”

Section: Introductionmentioning

confidence: 99%

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Introductory Chapter: Toward Near-Natural Assistive Devices

Burns¹,

Schumacher²,

Vinjamuri³

2018

Biomimetic Prosthetics

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“…More sophisticated approaches are needed to improve these prosthetic devices in order to reduce dependence on external control mechanisms. Some approaches include adding accelerometer data to the inputs [34], or even employing electroencephalogram (EEG) inputs [35].…”

Section: Gesture Recognitionmentioning

confidence: 99%

Longitudinal tracking of physiological state with electromyographic signals.

Stallard¹

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EEG-based brain controlled prosthetic arm

Cited by 83 publications

References 6 publications

Wink based facial expression classification using machine learning approach

Wink based facial expression classification using machine learning approach

Introductory Chapter: Toward Near-Natural Assistive Devices

Longitudinal tracking of physiological state with electromyographic signals.

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