Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network

Widadi, Rahmat; Widodo, Bongga Arif; Zulherman, Dodi

doi:10.33633/tc.v19i4.4119

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…The pooling methods commonly used are max pooling and average pooling. Max pooling will take the maximum value of the output value in the filter, while average pooling will take the average value [1].…”

Section: Pooling Layermentioning

confidence: 99%

“…In the health sector, the implementation of bio-signal processing such as electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) provides many benefits. EEG signals can be used in the rehabilitation of patients who have problems with neural motor disorders [1]. EEG signals are used in research on brain activity.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the test, the CNN method can be used and implemented in authentication detection based on the P300 signal. movement classification system using the Convolutional Neural Network (CNN) was conducted by [1] and resulted in an accuracy of 51.711%. Therefore, from the problems above, in this study, we will use the P300 signal with the Convolutional Neural Network (CNN) algorithm to detect spelling which can help users who have problems with motor and speech abilities, and calculate the performance of the algorithm in completing this research.…”

Section: Introductionmentioning

confidence: 99%

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Spelling Detection based on P300 Signal with Convolutional Neural Network (CNN) Algorithm

Muharrom,

Primartha

2022

SJIA

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Brain Computer Interface (BCI) is a system that connects the human brain with the outside world for people who have motor skills disability problems. One form of utilization is the P300 speller which is used for character recognition or detection by classifying the P300 signal. The Convolutional Neural Network (CNN) method is a deep learning method that can be used to handle signal problems with ID-CNN. At the initial stage the data signal will be transformed and followed by a duplication process using RandomOverSampling because the amount of data in each class is not balanced. The data will be divided into training, validation, and test data. After that, a training with CNN will be conducted and followed by an evaluation to find the best model. The test results from this study are a good-fitting CNN model with an evaluation value consisting of an accuracy of 94.27%, precision of 90.64%, sensitivity / recall of 98.30%, and f-measure of 94.31%. Based on the test, the CNN method can be used and implemented in authentication detection based on the P300 signal.

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Section: Pooling Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spelling Detection based on P300 Signal with Convolutional Neural Network (CNN) Algorithm

Muharrom,

Primartha

2022

SJIA

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“…Then the output of the neuron is entered into the activation function. The activation function used in MLP is ReLu (R Widadi, B. A. Widodo, and D. Zulherman, 2020)…”

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confidence: 99%

Deep Learning on EEG Study Concentration in Pendemic

Pamiela

Azhari

2021

JIM

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Brainwaves are one of the biometric properties that can be used to identify individuals based on their physical and behavioural characteristics. An electroencephalogram (EEG) can be used to measure and capture brain wave activity. The activities required are in the form of giving complex tasks to get thinking and concentration processes called Cognitive Tests, in the form of a Culture Fair Intelligence Test (CFIT) stimulus and Competency Test (UK). This study aims to obtain a pattern of the relationship between concentration and learning outcomes for late adolescent students during the pandemic. The object of research involved in this research is the 10th grade students of TKJ SMK. Data acquisition was carried out twice on the Beta signal by doing cognitive test questions which were done twice at school and at home. Then the data obtained from the test results will be extracted using Fast Fourier Transform (FFT). Furthermore, after the data extraction results are obtained, the classification process will be carried out using the CNN algorithm. The results of the FFT obtained the average value of the signal peak. The results of the CNN classification show that the pandemic does not affect student concentration. The average signal concentration in schools when testing using CFIT is 0.2445 and at the time of testing using UK Mathematics is 0.1330 with an average CFIT score of 77.05 and for UK average is 53.33 with an accuracy value of 83.33 %. While the average signal concentration at home when testing using CFIT is 0.2252 and at the time of testing using UK Mathematics is 0.1301 with an average CFIT score of 77.13 and for UK average is 57.50 with an accuracy value of 83, 33%.

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Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network

Cited by 2 publications

References 9 publications

Spelling Detection based on P300 Signal with Convolutional Neural Network (CNN) Algorithm

Spelling Detection based on P300 Signal with Convolutional Neural Network (CNN) Algorithm

Deep Learning on EEG Study Concentration in Pendemic

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