Innovative Approach to Control Wheelchair for Disabled People Using BCI

Lahane, Prashant; Adavadkar, Swarada Pushkar; Tendulkar, Sarvesh Vilas; Shah, Bhoomi Viralkumar; Singhal, Shubham

doi:10.1109/i2ct.2018.8529473

Cited by 15 publications

(8 citation statements)

References 1 publication

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“…The schematic model of a BCI system, as seen above, consists mainly of 4 parts [7,8,9,10,11].  The first part is the human brain which generates the brain activity signal.…”

Section: Figure 1 the Brain Computer Interface Systemmentioning

confidence: 99%

Brain Operated Wheelchair System

Manvi

Masood

Mohanchandra³

2020

ijai

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This paper predominantly explains the use of a simplistic uni-polar device to obtain EEG for the development of a Brain-Computer Interface (BCI). In contrast, BCI's eye-blinking stimuli can also be obtained. Consequently, focus and eye-blinking stimuli can be captured as control pulses in electric wheelchairs via a computer interface and electrical interface. This survey paper aims to provide a feasible solution to integrate a Brain-Computer Interface (BCI) with automated identification and avoidance of obstacles. The automated obstacle detection and avoidance system aims to provide a way to easily detect obstacles and easily correct the course.

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“…The schematic model of a BCI system, as seen above, consists mainly of 4 parts [7,8,9,10,11].  The first part is the human brain which generates the brain activity signal.…”

Section: Figure 1 the Brain Computer Interface Systemmentioning

confidence: 99%

Brain Operated Wheelchair System

Manvi

Masood

Mohanchandra³

2020

ijai

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show abstract

“…Research studies based on BCI focusing on the medical domain include interactive platforms for people suffering from severe motor dysfunction, designed especially for people with paralysis pertaining to motor functions (Bhemjibhaih et al. , 2018), controllable wheelchairs based on electroencephalography (EEG) signals for disabled patients (Lahane et al. , 2018), approaches for neuro-rehabilitation after a stroke (Ang et al.…”

Section: Introductionmentioning

confidence: 99%

“…The healthcare industry has also been greatly benefitted after the introduction of the BCI. Research studies based on BCI focusing on the medical domain include interactive platforms for people suffering from severe motor dysfunction, designed especially for people with paralysis pertaining to motor functions (Bhemjibhaih et al, 2018), controllable wheelchairs based on electroencephalography (EEG) signals for disabled patients (Lahane et al, 2018), approaches for neuro-rehabilitation after a stroke (Ang et al, 2010), diagnosing diseases like Alzheimer's (Pan et al, 2018) and the emotional analysis of people suffering from Schizophrenia, Attention deficit hyperactivity disorder (ADHD) or Parkinson's disease (Tononi and Edelman, 1998). BCI also holds immense potential in reliably monitoring the mental state of a person, classifying stress and examining patients suffering from posttraumatic experiences, depression, anxiety or studying their rehabilitation during motor functions in a qualitative manner.…”

Section: Introductionmentioning

confidence: 99%

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

Shankhdhar

Verma

Agrawal

et al. 2022

IJQRM

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PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

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“…This study proposes a cross-correlation method to obtain the coefficients in the frequency domain from consecutive frame samples to process the output to the control system using a machine learning algorithm for the movement of the wheelchair. The results indicated that the μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 55%.another paper proposed by (Lahane et al 2018) uses Body-Computer Interface (BCI) as an innovative approach to control the movement of the wheelchair for the disabled and paralyzed without any assistance.Our team has extensive knowledge and research experience that has translate into high quality publications (Bhansali et Unaccomplished accuracy rate of Electroencephalogram (EEG) controlled wheelchair movements which makes it unreliable for use by the disabled is the major key point that motivated the work on this project to accomplish the nearest accuracy rate of EEG controlled wheelchair movement for the disabled and paralyzed. The authors are expertized in the field of electronics and communication and were able to conduct studies in comparison between ATMEGA-328 microcontroller and Raspberry Pi microcontroller in the biomedical aspect.The main aim is to attain maximum accuracy of the EEG controlled wheelchair movement to make it reliable for the disabled and paralyzed individuals.…”

Section: Introductionmentioning

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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Innovative Approach to Control Wheelchair for Disabled People Using BCI

Cited by 15 publications

References 1 publication

Brain Operated Wheelchair System

Brain Operated Wheelchair System

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

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

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