The Survival of Intellectual Disabled Subjects in Social Environment Using BCI

Ashok, Vikas; Karthik, R.; Keerthana, K.; Roshinee, A. R.

doi:10.1109/i2c2sw45816.2018.8997133

Cited by 11 publications

(2 citation statements)

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“…It uses a special type of filtering technique called as Complex valued filtering which decomposes the given input image into real and imaginary parts. These real and imaginary parts give the magnitude and phase information (Mahidar et al, 2011;Ashok et al, 2018). Then for each pair of sub bands the sum and difference are calculated (Bai & Jin, 2015).…”

Section: Dual Tree Complex Wavelet Transform (Dtcwt)mentioning

confidence: 99%

Development of 3-stage hybrid computer aided design (3-HCAD) system for multi-modal medical images to identify brain tumor

Vajravelu

Tamilselvan

Mahadi

et al. 2022

ijhs

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The latest developments in medical imaging and computer-aided solutions for image processing problems attract attention of various researchers to impart their research in the medical imaging field. Designing and developing efficient algorithms to present the medical information effectively have become critical areas of research in this field. A 3-Stage Hybrid Computer Aided Design system is introduced to identify Brain tumor in earlier stages by extracting meaningful information from multimodal medical images. The preferred multi-modality images is Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The CAD system proposed in this paper can eliminate the dependency on human operators as it is an efficient software-based system. The first stage in this model consists of image pre-processing with Wavelet and Curvelet transforms. The second stage of the CAD system is an image segmentation process, which involves a combination of Wavelet Transform and Watershed Technique. The third stage involves image fusion, where the individually segmented CT and MRI images are fused together to obtain an integrated complementary information from two different images. This is followed by decomposing CT and MRI images using the Dual Tree Complex Wavelet Transform (DTCWT) and Nonsubsampled Contourlet Transform (NSCT).

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Section: Dual Tree Complex Wavelet Transform (Dtcwt)mentioning

confidence: 99%

Development of 3-stage hybrid computer aided design (3-HCAD) system for multi-modal medical images to identify brain tumor

Vajravelu

Tamilselvan

Mahadi

et al. 2022

ijhs

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“…Electroencephalography (EEG) is a noninvasive method of measuring cortical electrical activities that provides highly detailed temporal information reflecting the dynamics of brain activities [1,2] because of its portability, high mobility, simple setup, and low cost. By incorporating the nickel nitroprusside (NNP) nanoparticles with GNP/FE electrode fabrication, the efficiency is improved and the cost can be reduced [3], which is why it has seen extensive application in BCI studies [4,5]. Traditional Ag/AgCl electrodes bathed in the conductive gel are frequently used to collect trustworthy EEG data.…”

Section: Introductionmentioning

confidence: 99%

Nanocomposite-Based Electrode Structures for EEG Signal Acquisition

et al. 2022

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Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.

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