Electroencephalogram-Based OpenBCI Devices for Disabled People

Samson, Vemuri Richard Ranjan; Kitti, B. Praveen; Kumar, Sandeep; Babu, D. Suresh; Monica, Ch.

doi:10.1007/978-981-10-4280-5_24

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…OpenBCI has been used previously to predict the amplitude modulation of steady-state, visually evoked potentials signals with a single electrode [ 45 ]. OpenBCI has been used to develop EEG-based applications, including a device for disabled people [ 46 ], and to assess P3, N2, and FRN components for performance monitoring [ 47 ]. In a comparison of dry and wet electrode EEG systems, the dry electrode device was found to be more robust to 50 Hz line noise, less sensitive to electromagnetic interference, and useful for self-application and home usage [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

2022

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Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

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Section: Methodsmentioning

confidence: 99%

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

2022

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“…g.Tec equipment was used in 54 articles for the EEG method , 33 articles used Emotiv equipment , and 24 articles used Compumedics Neuroscan equipment . Further, 16 articles used Brain Products equipment [191][192][193][194][195][196][197][198][199][200][201][202][203][204][205][206], 13 articles used NeuroSky equipment [207][208][209][210][211][212][213][214][215][216][217][218][219], and Neuroelectrics [220][221][222][223][224][225][226][227] and OpenBCI [228][229][230][231][232][233][234][235] equipment were used in eight articles each. Moreover, seven articles used Biosemi equipment [236][237]…”

Section: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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“…The BCI device that was used, in order to capture the alpha brainwaves during the developed experimental procedure, is the OpenBCI Ganglion [25], which is shown in Figure 3. This board has 4 available input channels and samples data at 200 Hz.…”

Section: Bci Unitmentioning

confidence: 99%

“…Brainwaves are detected in the frequency domain, having signal intensity measured in microvolts (µV) and signal frequency usually ranging from 1 to 100 Hz. According to their frequency, there are specific bands classified as delta (δ) (1-4 Hz), theta (θ) (4-7 Hz), alpha (α) (8)(9)(10)(11)(12)(13), beta (β) (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (γ) (>30 Hz) [4]. evaluation and require extensive training.…”

Section: Introductionmentioning

confidence: 99%

Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

et al. 2019

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Modern achievements accomplished in both cognitive neuroscience and human-machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain-Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes' blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain-Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.A Brain-Computer Interface (BCI) is a system that enables communication between brain and machines. A BCI, in order to perform its purposes, records brain signals, interprets them, and produces corresponding commands to a connected machine [5]. BCI technology is used in various applications, such as security and authentication, education, neuromarketing and advertisement, games and entertainment, and several medical applications, such as cognitive neuroscience, brain-related prevention and diagnosis of health problems, rehabilitation, and restoration [6][7][8][9].This article presents the development of a BCI-based system that performs the motion control of a robotic vehicle by using brainwaves of a human operator. After capturing the brainwaves via EEG, a set of features is extracted and given as input to a neural network, which is trained to predict the desired movement of the robotic vehicle. The rest of this paper is organized as follows: In Section 2, the theoretical background of the research carried out is set up. In Section 3, the structure and operation of the proposed system are explained. In Section 4, the performance of the system is evaluated through the description of the experimental tests made, and the presentation of the corresponding results and discussion on them. Finally, Section 5 concludes the article and proposes future research work. Theoretical Background BCI TypesA BCI provides an interconnection platform that supports the full duplex communication between the brain and an external device. According to the way that BCIs use to set up the brain-device interconnection, they are classified as non-invasive or invasive. Non-invasive BCIs use electrodes placed on the scalp. They are easy and safe to use, low-cost, portable, and offer a relatively hig...

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Electroencephalogram-Based OpenBCI Devices for Disabled People

Cited by 12 publications

References 6 publications

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

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