A Mind Operated Computer Mouse Using Discrete Wavelet Transforms for Elderly People with Multiple Disabilities

Vasavi, K. Padma; Raju, P. Radhika; Radhika, S.; Prasad, Girijesh

doi:10.1016/j.procs.2016.05.205

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Jadi, rekonstruksi parsial sinyal EEG dilakukan dengan menerapkan wavelet terbalik hanya mengubah pada koefisien di mana konsentrasi energi sangat tinggi. Dengan demikian, sinyal EEG dinolkan menggunakan DWT [8].…”

Section: E Discreate Wavelet Transformunclassified

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

Pribadi

2021

PELS

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Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.

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Section: E Discreate Wavelet Transformunclassified

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

Pribadi

2021

PELS

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“…ese signals were used to control the machines in the lack of a normal channel. Some of the important BCIs that help paralyzed people to behave like normal people include mouse controller [9][10][11], speech synthesizer [12][13][14][15], robotic arm [16], hand controller [17], keyboard and mouse controller [18], facial expression detector [19], game controller [20], and mobile phone controller [21]. In our study, we planned to conduct a comparative study between two age groups, 20-28 and 29-40, to inspect the performance in offline and online modes using a recurrent neural network trained with a metaheuristic algorithm.…”

Section: Introductionmentioning

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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Cerebral palsy-affected individuals' brain-computer interface for wheelchair movement in an indoor environment using mental tasks

Ramakrishnan

2024

Egyptian Informatics Journal

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A Mind Operated Computer Mouse Using Discrete Wavelet Transforms for Elderly People with Multiple Disabilities

Cited by 3 publications

References 4 publications

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

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

Cerebral palsy-affected individuals' brain-computer interface for wheelchair movement in an indoor environment using mental tasks

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