scholarly journals Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Alexander Astaras ◽  
Nikolaos Moustakas ◽  
Alkinoos Athanasiou ◽  
Aristides Gogoussis
Keyword(s):  
Degrees Of Freedom ◽  
Cost Minimization ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Robotic Arm ◽  
Combined System ◽  
Position Sensing ◽  
Dry Electrode ◽  
Smooth Movement

Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization.Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. Commercially available dry electrode BCI headsets were evaluated. A particular headset model has been selected and is currently being integrated into the hybrid system.Results and Discussion. The combined arm-harness system has been successfully tested and met its design targets for speed, smooth movement, and immersive control. Initial tests verify that an operator using the system can perform pick and place tasks following a rather short learning curve. Further evaluation experiments are planned for the integrated BCI-harness hybrid setup.Conclusions. It is possible to design a portable robotic arm interface comparable in size, dexterity, speed, and fluidity to the human arm at relatively low cost. The combined system achieved its design goals for intuitive and immersive robotic control and is currently being further developed into a hybrid BCI system for comparative experiments.

scholarly journals A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Electrode System ◽  
Computer Interface ◽  
Robotic Arm ◽  
Robotic Hand

Brain-Computer Interface (BCI) is atechnology that enables a human to communicate with anexternal stratagem to achieve the desired result. This paperpresents a Motor Imagery (MI) – Electroencephalography(EEG) signal based robotic hand movements of lifting anddropping of an external robotic arm. The MI-EEG signalswere extracted using a 3-channel electrode system with theAD8232 amplifier. The electrodes were placed on threelocations, namely, C3, C4, and right mastoid. Signalprocessing methods namely, Butterworth filter and Sym-9Wavelet Packet Decomposition (WPD) were applied on theextracted EEG signals to de-noise the raw EEG signal.Statistical features like entropy, variance, standarddeviation, covariance, and spectral centroid were extractedfrom the de-noised signals. The statistical features werethen applied to train a Multi-Layer Perceptron (MLP) -Deep Neural Network (DNN) to classify the hand movementinto two classes; ‘No Hand Movement’ and ’HandMovement’. The resultant k-fold cross-validated accuracyachieved was 85.41% and other classification metrics, suchas precision, recall sensitivity, specificity, and F1 Score werealso calculated. The trained model was interfaced withArduino to move the robotic arm according to the classpredicted by the DNN model in a real-time environment.The proposed end to end low-cost deep learning frameworkprovides a substantial improvement in real-time BCI.

Cerebral Pulse Controlled Robotic Hand for Effective Paralytic Movements

2021 ◽  
Vol 9 (8) ◽  
pp. 2201-2204
Author(s):  
Abhay Patil
Keyword(s):  
Basic Assumption ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Handicapped People ◽  
Alpha Beta

Abstract: There are roughly 21 million handicapped people in India, which is comparable to 2.2% of the complete populace. These people are affected by various neuromuscular problems. To empower them to articulate their thoughts, one can supply them with elective and augmentative correspondence. For this, a Brain-Computer Interface framework (BCI) has been assembled to manage this specific need. The basic assumption of the venture reports the plan, working just as a testing impersonation of a man's arm which is intended to be powerfully just as kinematically exact. The conveyed gadget attempts to take after the movement of the human hand by investigating the signs delivered by cerebrum waves. The cerebrum waves are really detected by sensors in the Neurosky headset and produce alpha, beta, and gamma signals. Then, at that point, this sign is examined by the microcontroller and is then acquired onto the engineered hand by means of servo engines. A patient that experiences an amputee underneath the elbow can acquire from this specific biomechanical arm. Keywords: Brainwaves, Brain Computer Interface, Arduino, EEG sensor, Neurosky Mindwave Headset, Robotic arm

A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

2018 ◽  
Author(s):  
Shivanthan A.C. Yohanandan ◽  
Isabell Kiral-Kornek ◽  
Jianbin Tang ◽  
Benjamin S. Mshford ◽  
Umar Asif ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface

Brain.me: A Low-Cost Brain Computer Interface for AAL Applications

2014 ◽  
pp. 223-231
Author(s):  
Niccolò Mora ◽  
V. Bianchi ◽  
I. De Munari ◽  
P. Ciampolini
Keyword(s):  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface
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