The EEG brain signal representation for surfaces and shapes touching behavior with an inexpensive device

Lawpradit, Wachira; Yooyativong, Thongchai

doi:10.1109/ecti-ncon.2018.8378297

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…These results are similar to the ones from the previous spectrum analysis. But, delta, theta, low-alpha and highalpha show in high value when touching learning [22]. P-value of these waves are not significant for blinded people as supposed to normal people.…”

Section: Resultsmentioning

confidence: 80%

“…The Fast Fourier Transform results show the amplitude of each waveband spectrum which is important for finding the signal patterns of textile picture touching. Regarding general wave in EEG signal i.e., delta, theta, low-alpha, high-alpha, low-beta, high-beta, lowgamma, mid-gamma, and high-gamma [22], the EEG spectrum analysis show that the dominant EEG waves that response to picture touching activity are delta, theta, high-alpha and mid-gamma. Some examples of overall results from twenty volunteers, selected for only one normal volunteer (N1) and one blinded volunteer (B5), were shown in Figures 8 to 11 for explanation.…”

Section: Resultsmentioning

confidence: 99%

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The EEG Brain Signal Pattern Analysis During Touching Learning of the Blind and Normal People via a Low-cost Device

Lawpradit

Yooyativong

Chaisricharoen

2022

JMM

Self Cite

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This research was designed for analyse and compare patterns of EEG signals while blinded and normal people performing touching-leaning. The pattern analysis focuses on important EEG wavebands during touching, such as delta, theta, alpha and gamma waveband. The EEG waveband-datasets were detected and recorded with inexpensive device, the NeuroSky Mindwave, where it is connected to a computer for data analysis through Bluetooth communication and had electric noise reduction chipset inside. The analysis for the EEG waveband pattern-comparisons is performed by utilizing waveband power spectrum and statistical technique based on FFT algorithm, Area Under Curve (AUC), mean, S.D., T-score, and P-value testing. The experiment shown that dominant EEG signal wavebands of blinded people when touching-learning are delta, theta, alpha and gamma. These wavebands were higher than normal people. Moreover, by using statistical analysis T-score and P-values testing, analyzed results illustrate that normal and blinded people EEG wave patterns are significantly different on gamma waveband where blinded people have significantly higher gramma wave during touching-learning activities. The objects used for touching-leaning in the experiments are square, triangle, circle, and hexagon shape of tactile pictures. The observation also shown that blinded people use their muscle in movement more than normal people which also strongly related to gramma wave. In addition, another wave did not relate to statistic significant. This result illustrated to normal and blinded people thinking and imagination with the same pattern.

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Section: Resultsmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

The EEG Brain Signal Pattern Analysis During Touching Learning of the Blind and Normal People via a Low-cost Device

Lawpradit

Yooyativong

Chaisricharoen

2022

JMM

Self Cite

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“…The rhythmic activity is divided into wave bands by frequency while the transient is referring to spontaneous spikes and wave formations that are sharp [23]. There are five types at the most critical frequency ranges: delta (2-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (15-30Hz), and gamma (30-80Hz) [6,24].…”

Section: Features Of Eeg Signalsmentioning

confidence: 99%

“…Furthermore, regulating and controlling the human behaviours; regulating and controlling physical actions; regulating memory functions; the method of thinking and other reasoning processes [5]. Medical professionals use Electroencephalography (EEG) to diagnose abnormalities in human life [6]. EEG is an electrophysiological monitoring technique that logs the electrical signals emanating from the brain.…”

Section: Introductionmentioning

confidence: 99%

Brain computer interface based smart keyboard using neurosky mindwave headset

Salih

Abdal

2020

TELKOMNIKA

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In the last decade, numerous researches in the field of electro-encephalography (EEG) and brain-computer-interface (BCI) have been accomplished. BCI has been developed to aid disabled/partially disabled people to efficiently communicate with the community. This paper presents a control tool using the Neurosky Mindwave headset, which detects brainwaves (voluntary blinks and attention) to form a braincomputer interface (BCI) by receiving the system signals from the frontal lobe. This paper proposed an alternative computer input device for those disabled people (who are physically challenged) rather than the conventional one. The work suggested to use two virtual keyboard designs. The conducted experiment revealed a significant result in developing user printing skills on PCs. Encouraging results (1.55-1.8 word per minute (WPM)) were obtained in this research in comparison to other studies.

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“…All Neurosky sensor products enclose a Think Gear chip that enables that works as a link between human brain and the robotic system. Its value ranges from 0-256 and operating frequency 0.05 0.5 Hz [12]. Some Specifications of Neurosky include its frequency range from 2.42-2.472 GHz and its power is 50mW with a Sampling rate of 512Hz for the EEG signal.…”

Section: Brainwave Sensormentioning

confidence: 99%

Brain Computer Interface for Controlling Mobile Robot

2019

ijrte

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This Paper describes a brainwave controlled mobile robot based on brain computer interface (bci). This uses Matlab to analyze the brainwaves. Bci bypass the communication path between brain and computer to establish a communication system in which the data flows directly from brain to physical devices. Bci consists of software and hardware components that allow brain inputs to be translated as controls for machine. The different brain patterns are translated into different machine commands. These obtained commands are used to control the mobile robot. This technology is widely used in neuro-prosthetic devices, which helps people in achieving or improving their capability. . Our intention in this paper is to develop a smart wheelchair which will help disabled people in their mobility. The Bci consists of Neurosky brainwave headset which is connected to computer for extracting the data and to process real time commands, which is then used to control the module.

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The EEG brain signal representation for surfaces and shapes touching behavior with an inexpensive device

Cited by 3 publications

References 7 publications

The EEG Brain Signal Pattern Analysis During Touching Learning of the Blind and Normal People via a Low-cost Device

The EEG Brain Signal Pattern Analysis During Touching Learning of the Blind and Normal People via a Low-cost Device

Brain computer interface based smart keyboard using neurosky mindwave headset

Brain Computer Interface for Controlling Mobile Robot

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