scholarly journals EEG Controlled Exoskeleton

2021 ◽  
Vol 5 (2) ◽  
pp. 13-18
Author(s):  
Dr. Pandya Vyomal N ◽  
Sreenivasulu Dr. Y. ◽  
Jayaprakasan Dr. V.
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Time Data ◽  
Output Device ◽  
Brain Signals ◽  
Real Time Data ◽  
Standing Up ◽  
Direction Of Movement ◽  
Computer Based ◽  
Bci System

A physically paralyzed person is doomed to stay on a chair or the bed making him completely at mercy of a helper. To help these patients this device uses a Brain Computer Interface (BCI). A brain-computer interface (BCI) is a computer-based system that acquires brain signals, analyses them, and translates them into commands that are relayed to an output device to carry out a desired action. In principle, any type of brain signal could be used to control a BCI system. This device is an Exoskeleton made of steel/iron that will be attached the Patient who is Physically Paralyzed. An exoskeleton is the external skeleton that supports and protects an animal's body, in contrast to the internal skeleton (endoskeleton) of, for example, a human. It will give support to the Patient while standing up so that he/she doesn’t fall down. The Brainwaves being read by the EEG will be fed to a neural networking model coded using Google’s Tensor Flow which will create a model with a high enough accuracy to understand the intentions of the patient i.e. whether to stand up or not and the direction of movement. The Real-time data will be given to the existing model and the motors of the exoskeleton will move accordingly.

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
M. K.M Rahman ◽  
Md. A. Mannan Joadder
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Quantitative Comparison ◽  
Computer Interface ◽  
Eeg Signal ◽  
Brain Signals ◽  
One Step ◽  
Overall Performance ◽  
Processing Steps ◽  
Bci System

Motor Imagery (MI) is a voluntary modulation of brain signals for specific action without real limb movement. It is essential to classify MI signal to design a brain computer interface (BCI). BCI involves a number of signal processing steps, and a lot of techniques have been developed for each step. There can be numerous combinations of these techniques at different steps that can be employed to design a BCI. This work focuses on MI-based BCI using EEG signal and reviews the existing techniques. More importantly, a detailed comparative study is performed to explore the important combinations of methods by comparing their performance quantitatively. Often a method, which performs very good in one combination, can be bad performer in other combinations and it is a dilemma for the researchers to select appropriate methods for their desired BCI application.In our performance analysis, we have systematically included the variations of methods in each step of BCI such that it gives idea to BCI researchers how each method in one step fits best with specific combinations of methods in other steps. We have shown that how much each step is sensitive towards overall performance of the BCI system.We hope that this work helps, especially for new researchers, to provide a better guideline for designing more efficient BCI system.

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 The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

scholarly journals A Survey of Interactive Systems based on Brain-Computer Interfaces

2013 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alessandro Luiz Stamatto Ferreira ◽  
Leonardo Cunha de Miranda ◽  
Erica Esteves Cunha de Miranda ◽  
Sarah Gomes Sakamoto
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interface ◽  
Research Area ◽  
Interactive Systems ◽  
Computer Interface ◽  
Immersive Environments ◽  
User Interactions ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Near Future

Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

scholarly journals Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingwei Zhang ◽  
Yao Hou ◽  
Rongnian Tang ◽  
Youjun Li
Keyword(s):  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Spatial Covariance ◽  
Learning Machine ◽  
Low Dimensional ◽  
Bci System

In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

Stroke Rehabilitation and Parkinson's Disease Tremor Reduction Using BCIs Combined With FES

2021 ◽  
pp. 473-491
Author(s):  
Sophie V. Adama ◽  
Martin Bogdan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Subjects ◽  
Stroke Rehabilitation ◽  
Brain Computer Interface ◽  
Electrical Muscle Stimulation ◽  
Brain Signals ◽  
First Case ◽  
Bci System ◽  
Electrical Muscle

This article describes how Stroke and Parkinson's disease are two illnesses that particularly affect motor functions. With the advancements in technology, there is a lot of research focusing on finding solutions: to contribute to neuroplasticity in the first case, and to reduce symptoms in the second case. This manuscript describes the design of a brain-computer interface system (BCI) system paired with an electrical muscle stimulation suit for stroke rehabilitation and the reduction of tremors caused by Parkinson's disease. The idea is to strengthen the sensory-motor feedback loop, which will allow a more stabilized control of the affected extremities by taking into account the patient's motivation. To do so, his brain signals are measured to detect his intention to attempt to execute a movement, in contrast to the classical approach where the movement executions are imposed. A first feasibility study was completed. The author's next step is planning to test the system first with healthy subjects and finally with patients.

scholarly journals The point of no return in vetoing self-initiated movements

2015 ◽  
Vol 113 (4) ◽  
pp. 1080-1085 ◽  
Author(s):  
Matthias Schultze-Kraft ◽  
Daniel Birman ◽  
Marco Rusconi ◽  
Carsten Allefeld ◽  
Kai Görgen ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Readiness Potential ◽  
Movement Onset ◽  
Brain Signals ◽  
Spontaneous Movements ◽  
Point Of No Return

In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain–computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.

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