Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Li, Junwei; Ramkumar, S.; Emayavaramban, G.; Vinod, D. Franklin; Thilagaraj, M.; Muneeswaran, V.; Rajasekaran, M. Pallikonda; Venkataraman, Vinay; Hussein, Ahmed Faeq

doi:10.1109/access.2018.2886708

Cited by 33 publications

(15 citation statements)

References 40 publications

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“…An analysis of upper limb movements in the timedomain of low-frequency electroencephalography (EEG) signals was carried out in [42]. Brain signals are analyzed using band power and radial basis function and implemented on the wheel chair [43]. A non-contact control system is designed that allows the paralyzed patients to get assistance in the hospital by activating the nurse emergency system and adjusting other appliances.…”

Section: Related Workmentioning

confidence: 99%

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Vinoj

Jacob²,

Menon³

et al. 2019

IEEE Access

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Stroke is a standout amongst the most imperative reasons of incapacity on the planet. Due to partial or full paralysis, the majority of patients are compelled to rely upon parental figures and caregivers in residual life. With post-stroke rehabilitation, different types of assistive technologies have been proposed to offer developments to the influenced body parts of the incapacitated. In a large portion of these devices, the clients neither have control over the tasks nor can get feedback concerning the status of the exoskeleton. Additionally, there is no arrangement to detect user movements or accidental fall. The proposed framework tackles these issues utilizing a brain-controlled lower limb exoskeleton (BCLLE) in which the exoskeleton movements are controlled based on user intentions. An adaptive mechanism based on sensory feedback is integrated to reduce the system false rate. The BCLLE uses a flexible design which can be customized according to the degree of disability. The exoskeleton is modeled according to the human body anatomy, which makes it a perfect fit for the affected body part. The BCLLE system also automatically identifies the status of the paralyzed person and transmits information securely using Novel-T Symmetric Encryption Algorithm (NTSA) to caregivers in case of emergencies. The exoskeleton is fitted with motors which are controlled by the brain waves of the user with an electroencephalogram (EEG) headset. The EEG headset captures the human intentions based on the signals acquired from the brain. The brain-computer interface converts these signals into digital data and is interfaced with the motors via a microcontroller. The microcontroller controls the high torque motors connected to the exoskeleton's joints based on user intentions. Classification accuracy of more than 80% is obtained with our proposed method which is much higher compared with all existing solutions. INDEX TERMS Artificial skin, assistive technologies, brain-computer interface (BCI), electroencephalogram (EEG), brain-controlled exoskeleton, paralyzed, stroke.

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Section: Related Workmentioning

confidence: 99%

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Vinoj

Jacob²,

Menon³

et al. 2019

IEEE Access

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“…Of the papers reviewed, 81% (54 papers) presented results that included an asynchronous control paradigm, while the rest presented results only for synchronous control paradigms. Although results generated using synchronous control could achieve high accuracies [ 24 , 25 ], they increase the latencies experienced by the subject and are not feasible for many practical BCIs; in particular, for the brain control of dynamic devices. This is because a synchronous control paradigm would lead to episodic movements in dynamic devices such as prosthetics, exoskeletons, and wheelchairs, which should ideally execute commands in real time to ensure smooth movement.…”

Section: Synchronous Vs Asynchronous Controlmentioning

confidence: 99%

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.

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“…Because there was no feedback connection between the three layers, all the three layers were moved only in the frontward direction from the input layer to the hidden layer and from the hidden layer to the output layer. Basically, FFNN was trained with default parameters with a default training algorithm [37]. But in our research, we planned to change the benchmark training algorithm to a bioinspired optimization algorithm called crow search algorithm (CSA) to optimize the neural network model to analyze the different patterns generated from the subjects during the classification process, and also we compared the results gathered in the study.…”

Section: Feed Forward Neural Network (Ffnn)mentioning

confidence: 99%

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

Thilagaraj

Ramkumar

Arunkumar

et al. 2022

Computational Intelligence and Neuroscience

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Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices. In this study, we planned to conduct our research with twenty subjects from different age groups from 20 to 28 and 29 to 40 using three-electrode systems to analyze the performance for developing a mobile robot for navigation using band power features and neural network architecture trained with a bioinspired algorithm. From the experiment, we recognized that the maximum classification performance was 94.66% for the young group and the minimum classification performance was 94.18% for the adult group. We conducted a recognizing accuracy test for the two contrasting age groups to interpret the individual performances. The study proved that the recognition accuracy was maximum for the young group and minimum for the adult group. Through the graphical user interface, we conducted an online test for the young and adult groups. From the online test, the same young-aged people performed highly and actively with an average accuracy of 94.00% compared with the adult people whose performance was 92.00%. From this experiment, we concluded that, due to the age factor, the signal generated by the subjects decreased slightly.

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Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Cited by 33 publications

References 40 publications

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

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