EEG Mind Controlled Smart Prosthetic Arm – A Comprehensive Study

In this study, Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, which is popular in many sectors (game, defense, sports, etc.), especially in medicine, was composed. In addition, a robot hand was designed to be integrated into the BCI system, especially to help partially or completely disabled individuals. For this purpose, feature extraction was performed using discrete wavelet transform (Db6) from SSVEP signals recorded from seven different frequencies (6, 6.5, 7, 7.5, 8.2, 9.3, 10 Hz) and four different individuals. Extracted features were classified by support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms. According to the classification results, the highest performance was obtained in the SVM algorithm with an accuracy of 84%.

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“…In addition, an analog notch filter was applied to the data obtained at interference frequency (50Hz) [8].…”

Section: A Dataset Descriptionmentioning

confidence: 99%

Design of Steady-State Visually-Evoked Potential Based Brain-Computer Interface System

Avci

Hamurcu

Bozbas³

et al. 2022

J Intell Syst Appl

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“…Data acquisition software named 'Multi-Shimmer Sync' developed by shimmer research group was used to synchronize all five shimmer modules and to transmit streamed data to PC through Bluetooth. Multi-Shimmer Sync is an application which allows for the configuration and synchronized data capture from multiple Shimmers ( Figure 5) [12], [13]. All five shimmer sensor modules are configured with ±1.5g acceleration range, this range is chosen because most of the smartphone's inbuilt accelerometers have same range.…”

Section: Wearable Accelerometer and Gyroscope Sensorsmentioning

confidence: 99%

Experimental Investigation of Human Gait Recognition Database using Wearable Sensors

Said¹,

Kork²,

Nair³

et al. 2018

Adv. sci. technol. eng. syst. j.

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In this research human gait database is collected using different possible methods such as Wearable sensors, Smartphone and Cameras. For a gait recognition accelerometer data from wearable shimmer modules and smartphone are used. Data from different sensors location is compared to know which sensor location have better recognition rate. Different walking scenarios like slow, normal and fast walk were investigated. Wearable sensors and smartphone data are compared to know whether mobile phones can be used for gait recognition or not. Also effects of age, height, weight on gait recognition are also studied. The obtained results of gait biometric matrices like Genuine Recognition Rate (GRR), Total Recognition Rate (TRR) and Equal Error Rate (EER) showed better results. EER in different walking scenarios ranged from 0.17% to 2.27% for the five wearable sensors at different locations, whereas EER results of smartphone data ranged from 1.23% to 4.07%. For sensors located at leg, pocket and hand the average GRR value falls with increase in age group, while for sensors located at upper pocket and bag, the GRR value doesn't follow any trend. Moreover GRR results on all sensors show no significance regarding height or weight variations.

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“…The system proved its effectiveness in decoding the hand movement of a patient who suffered from a stroke and used that signal to control a prosthetic hand. Another research implemented by Taha and his team (Beyrouthy et al, 2017) is about a system that extracts the EEG signals from the brain and uses them to control a smart 3D prosthetic arm. The system showed great results and presented a reliable alternative for an invasive system.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive study of EEG-based control of artificial arms

Satam¹

2023

Vojnotehnički glasnik

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Introduction/purpose: The electroencephalography (EEG) signal has a great impact on the development of prosthetic arm control technology. EEG signals are used as the main tool in functional investigations of human motion. The study of controlling prosthetic arms using brain signals is still in its early stages. Brain wave-controlled prosthetic arms have attracted researchers' attention in the last few years. Methods: Several studies have been carried out to systematically review published articles as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used in the prosthetic arm and other technologies. Results: 175 articles were studied, compared, and filtered to only include the articles that have strong connections to the study. Conclusion: This study has three goals. The first one is to gather, summarize, and evaluate information from the studies published between 2011 and 2022. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control of prosthetic arms to all experts and scientists. The third goal is to recognize the gap in knowledge that demands further investigation and to recommend directions for future research in this area.

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EEG Mind Controlled Smart Prosthetic Arm – A Comprehensive Study

Abstract: Recently, the field of prosthetics has seen many accomplishments especially with the integration of technological advancements. In this paper, different arm types (robotic, surgical, bionic, prosthetic and static)

Cited by 19 publications

References 6 publications

Design of Steady-State Visually-Evoked Potential Based Brain-Computer Interface System

Design of Steady-State Visually-Evoked Potential Based Brain-Computer Interface System

Experimental Investigation of Human Gait Recognition Database using Wearable Sensors

A comprehensive study of EEG-based control of artificial arms

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