Controlling the Direction of Wheelchair Movement Using Raspberry-Pi Based Brain Signals

Madona, Putri; Mujiono, Renndy Raldy; Wijaya, Yusmar Palapa

doi:10.1109/icae47758.2019.9221680

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…A recent study conducted by (Tiwari et al 2020) proposed that electroencephalography can be used to maneuver a miniature wheelchair using a sensitive Brain-Computer Interface along with a microcontroller to provide higher accuracy. Another study by (Al-Haddad et al 2012) states that wheelchairs can be controlled using eye gaze and eye blinks based on point bug algorithms.A paper proposed by (Madona, Mujiono, and Wijaya 2019) states that Raspberry Pi microcontroller can be used to control the direction of the wheelchair based on the brain signals obtained from EEG headset.…”

Section: Resultsmentioning

confidence: 99%

Comparative Analysis on EEG Controlled Wheelchair using Raspberry Pi and ATMEGA-328 Microcontroller

2022

pnr

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The aim of this research work is to analyze the accuracy of the ATMEGA-328 Microcontroller, used in Electroencephalogram(EEG) controlled wheelchairs, and to compare the accuracy rate between ATMEGA-328 and Raspberry Pi microcontroller. Materials and Methods: Data collection containing innovative wheelchair control movement output from the hardware designed was used in this research.samples were considered as (N=20) for ATMEGA-328 microcontroller , and (N=20) for Raspberry Pi microcontroller in accordance to total sample size calculated using clinical.com by keeping the alpha error threshold by 0.05, enrollment ratio as 0:1,95% confidence interval , power at 80%. Result: Comparison of accuracy rate is done by independent sample test using IBM-SPSS software.There is a statistical indifference between the output obtained from ATMEGA-328 (65.4%) showed higher results in comparison to Raspberry Pi (58.4%) with (p=0.080;p>0.05). Conclusion: EPOC headset appears to give better sensitivity than Neurosky mindwave portable headset in processing the EEG signals in an EEG controlled wheelchair.

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Section: Resultsmentioning

confidence: 99%

Comparative Analysis on EEG Controlled Wheelchair using Raspberry Pi and ATMEGA-328 Microcontroller

2022

pnr

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“…EEG signals can be measured, e.g., in basic medical research [ 111 , 112 ], for neurofeedback or biofeedback training, for medical applications, or in many cases be used as an HMI, mostly used to control prostheses, a robotic hand or a wheelchair, based on Arduino or Raspberry Pi [ 113 , 114 , 115 , 116 , 117 , 118 ]. This is why there is even an OpenEEG project suggesting hardware and software for EEG measurements [ 119 ].…”

Section: Eeg Measurementsmentioning

confidence: 99%

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.

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A Brain-Computer Interface for Controlling a Mobile Assistive Device by Using the NeuroSky EEG Headset and Raspberry Pi

Rușanu

2022

IFMBE Proceedings

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Controlling the Direction of Wheelchair Movement Using Raspberry-Pi Based Brain Signals

Cited by 5 publications

References 4 publications

Comparative Analysis on EEG Controlled Wheelchair using Raspberry Pi and ATMEGA-328 Microcontroller

Comparative Analysis on EEG Controlled Wheelchair using Raspberry Pi and ATMEGA-328 Microcontroller

Measuring Biosignals with Single Circuit Boards

A Brain-Computer Interface for Controlling a Mobile Assistive Device by Using the NeuroSky EEG Headset and Raspberry Pi

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