TOWARDS THE DEVELOPMENT OF A ELECTRO-ENCEPHALOGRAPHY BASED NEUROPROSTHETIC TERMINAL DEVICE

2015 ◽  
Vol 76 (4) ◽  
Author(s):  
Khairunnisa Johar ◽  
Cheng Yee Low ◽  
Fazah Akhtar Hanapiah ◽  
Ahmed Jaffar ◽  
Farhana Idris ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Motor Impairment ◽  
Cost Effective ◽  
Computer Interface ◽  
Prosthetic Hand ◽  
3D Animation ◽  
Eeg Signals ◽  
Recognition Phase ◽  
Severe Motor ◽  
Severe Motor Impairment

Brain-Computer Interface (BCI) using Electroencephalography (EEG) enables non-invasive direct control between human brain and machine and opens up new possibilities in providing healthcare solutions for people with severe motor impairment. This paper reviews the recent trends in neuroprostheses and presents a conceptual design for the development of a cost-effective neuroprosthetic hand deploying EEG signals. Towards the development of a brain-computer interface for neuroprostheses, EEG signals are recorded from healthy subjects using the Emotiv Suite Software. The recognition phase and signal analysis are performed using the EEGLab Software. Signal processing is required until clear rhythmic waves are obtained as a command to control a prosthetic hand. A Graphical User Interface (GUI) will be developed using Matlab Software and aided with 3D Animation as a medium of interaction for basic training for the patient before using the prosthetic hand.

scholarly journals Non motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment

2014 ◽  
Vol 8 ◽  
Author(s):  
Josef Faller ◽  
Reinhold Scherer ◽  
Elisabeth V. C. Friedrich ◽  
Ursula Costa ◽  
Eloy Opisso ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Motor Impairment ◽  
Computer Interface ◽  
Motor Tasks ◽  
Severe Motor ◽  
Severe Motor Impairment

scholarly journals A Co-Adaptive Brain-Computer Interface for End Users with Severe Motor Impairment

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e101168 ◽  
Author(s):  
Josef Faller ◽  
Reinhold Scherer ◽  
Ursula Costa ◽  
Eloy Opisso ◽  
Josep Medina ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Motor Impairment ◽  
End Users ◽  
Computer Interface ◽  
Severe Motor ◽  
Severe Motor Impairment

scholarly journals Dorsolateral prefrontal cortex-based control with an implanted brain–computer interface

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sacha Leinders ◽  
Mariska J. Vansteensel ◽  
Mariana P. Branco ◽  
Zac V. Freudenburg ◽  
Elmar G. M. Pels ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Dorsolateral Prefrontal Cortex ◽  
Brain Computer Interface ◽  
Motor Impairment ◽  
Computer Interface ◽  
Interface Control ◽  
Average Accuracy ◽  
Dorsolateral Prefrontal ◽  
Severe Motor ◽  
Severe Motor Impairment

Abstract The objective of this study was to test the feasibility of using the dorsolateral prefrontal cortex as a signal source for brain–computer interface control in people with severe motor impairment. We implanted two individuals with locked-in syndrome with a chronic brain–computer interface designed to restore independent communication. The implanted system (Utrecht NeuroProsthesis) included electrode strips placed subdurally over the dorsolateral prefrontal cortex. In both participants, counting backwards activated the dorsolateral prefrontal cortex consistently over the course of 47 and 22 months, respectively. Moreover, both participants were able to use this signal to control a cursor in one dimension, with average accuracy scores of 78 ± 9% (standard deviation) and 71 ± 11% (chance level: 50%), respectively. Brain–computer interface control based on dorsolateral prefrontal cortex activity is feasible in people with locked-in syndrome and may become of relevance for those unable to use sensorimotor signals for control.

scholarly journals State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review

2020 ◽  
Vol 08 (01) ◽  
pp. 12-25 ◽  
Author(s):  
Miaomiao Zhuang
Keyword(s):  
Cognitive Rehabilitation ◽  
Brain Computer Interface ◽  
Motor Impairment ◽  
Computer Interface ◽  
Spastic Cerebral Palsy ◽  
Non Invasive ◽  
Cord Injury ◽  
Communication Method ◽  
Severe Motor ◽  
Cognitive Diseases

Brain–computer interface (BCI) is a novel communication method between brain and machine. It enables signals from the human brain to influence or control external devices. Currently, much research interest is focused on the BCI-based neural rehabilitation of patients with motor and cognitive diseases. Over the decades, BCI has become an alternative treatment for motor and cognitive rehabilitation. Previous studies demonstrated the usefulness of BCI intervention in restoring motor function and recovery of the damaged brain. Electroencephalogram (EEG)-based BCI intervention could cast light on the mechanisms underlying neuroplasticity during upper limb recovery by providing feedback to the damaged brain. BCI could act as a useful tool to aid patients with daily communication and basic movement in severe motor loss cases like amyotrophic lateral sclerosis (ALS). Furthermore, recent findings have reported the therapeutic efficacy of BCI in people suffering from other diseases with different levels of motor impairment such as spastic cerebral palsy, neuropathic pain, etc. Besides motor functional recovery, BCI also plays its role in improving the behavior of patients with cognitive diseases like attention-deficit/hyperactivity disorder (ADHD). The BCI-based neurofeedback training is focused on either reducing the ratio of theta and beta rhythm, or enabling the patients to regulate their own slow cortical potentials, and both have made progress in increasing attention and alertness. With summary of several clinical studies with strong evidence, we present cutting edge results from the clinical application of BCI in motor and cognitive diseases, including stroke, spinal cord injury, ALS, and ADHD.

scholarly journals Brain-computer interface for post-stroke rehabilitation

2019 ◽  
pp. 251-256 ◽  
Author(s):  
Vadim Grubov ◽  
Artem Badarin ◽  
Nikolay Schukovsky ◽  
Anton Kiselev
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Motor Impairment ◽  
Computer Interface ◽  
Eeg Signals ◽  
Experimental Conditions ◽  
Rehabilitation Process ◽  
Post Stroke ◽  
Subsequent Rehabilitation

In the paper we proposed the approach for increasing of quality of neurorehabilitation of post-stroke patients based on wavelet analysis of EEG signals recoded during motor imagery. Also we proposed brain-computer interface based on the method. We determined all necessary procedures required to find motor imagery type (kinesthetic or visual) for each individual patient and described subsequent rehabilitation process. We tested developed brain-computer interface on 20 participants with post-stroke motor impairment. We believe that developed system can be used not only in laboratory experimental conditions, but also in clinical ones.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106826
Author(s):  
Giovanni Acampora ◽  
Pasquale Trinchese ◽  
Autilia Vitiello
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

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