Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Diwakar, Shyam; Bodda, Sandeep; Nutakki, Chaitanya; Vijayan, Asha; Achuthan, Krishnashree; Nair, Bipin

doi:10.5220/0005134802700275

Cited by 17 publications

(3 citation statements)

References 43 publications

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“…It also offers the user the possibility of taking off the EEG device when feeling inconvenience [6]. EEG-based BCI plays an essential role in the area of prosthesis control [7], managing the interaction between the patient and the device without requiring an invasive surgical procedure to reconnect nerves and allow amputees to control their prosthesis [6].…”

Section: Introductionmentioning

confidence: 99%

EEG-based Brain Computer Interface Prosthetic Hand using Raspberry Pi 4

Ali¹,

Popescu²,

Hadăr³

et al. 2021

IJACSA

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Accidents, wars, or different diseases can affect upper limbs in such a manner so their amputation is required, with dramatic effects on people's ability to perform tasks such as grabbing, holding objects, or moving them. In this context, it is necessary to develop solutions to support upper limb amputees to perform daily routine activities. BCI (brain-computer interface) offer the ability to use the neural activity of the brain to communicate or control robots, artificial limbs, or machines without physical movement. This article proposing an electroencephalography (EEG) mind-controlled prosthetic arm. It eliminates the drawbacks like the high price, heaviness, and dependency on the intact nerves related to the myoelectric and other types of prostheses currently in use. The developed prototype is a low-cost 3D-printed prosthetic arm controllable via brain commands using EEG-based BCI technology. It includes a stepper motor controlled by Raspberry Pi 4 to perform actions like open/close movement and holding objects.The project has successfully implemented and achieve the aim to create a prototype of a mind-controlled prosthetic arm system in addition to the necessary experimental tests and calculations regarding torque, force, and the weight that the hand can carry. The paper proves the feasibility of the approach and opens the route for improving the design of the prototype to attach it to the upper-limb amputation stump.

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Section: Introductionmentioning

confidence: 99%

EEG-based Brain Computer Interface Prosthetic Hand using Raspberry Pi 4

Ali¹,

Popescu²,

Hadăr³

et al. 2021

IJACSA

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“…Based on the modular organization and functioning of cerebellum, there are several models like spiking neural networks (SNN) [6], [7], artificial neural networks [8], [9] and internal models [10], [11] which have been used in control theory and artificial intelligence. Robotics is another key area where these models had been applied to perform real-time classification of bio-signals like electromyographic (EMG) and electroencephalographic (EEG) signals [12]. These signals were used as non-invasive measures for prosthetic limb movement which could restore the lost motor functions [13].…”

Section: Introductionmentioning

confidence: 99%

Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms

Medini

Zacharia

Nair

et al. 2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking networkbased pattern recognition, in this study, real world datasets were encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms.

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“…Alpha brain waves have been utilized[7] to control the devices. Beta and Mu rhythms have been used to control the prosthetic arms by the authors in[8]. The forward and the inverse kinematic behaviour of the indigenously developed robotic arm is also tested in this work.…”

mentioning

confidence: 99%

Home Automation Using Brain Signals

Mohan¹,

Mallikarjun²,

Beeresh³

et al. 2023

IJSRCSEIT

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In this time of digitization and computerization, the life of individuals is getting more straightforward as nearly everything is programmed. This interconnection of the things can be used to help people with physical disabilities including paralysis. The brain signals of such people can be harnessed to create a home automation system. In this work, the concentration levels of such individuals are extracted using the EEG signals. These signals are then used to control the electronic devices. The designed system has been tested to operate the switching of an electric bulb using brain signals. This system can also be extended to assist a physically impaired individual to have effective control over electrical and electronic appliances and devices within a home.

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Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Cited by 17 publications

References 43 publications

EEG-based Brain Computer Interface Prosthetic Hand using Raspberry Pi 4

EEG-based Brain Computer Interface Prosthetic Hand using Raspberry Pi 4

Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms

Home Automation Using Brain Signals

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