Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

Atmaji, Catur; Perwira, Zandy Yudha

doi:10.22146/ijeis.22893

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Salah satu metode ekstraksi ciri adalah dengan menggunakan nilai RMS pada sinyal biomedis seperti EMG dan EEG [14]. Nilai RMS merupakan nilai yang menunjukkan besar power sinyal.…”

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Klasifikasi Gerakan Jari Tangan Berdasarkan Sinyal Electromyogram Pada Lengan

Atmaji

Santoso

Hujja

et al. 2021

Indonesian J. Electron. Instrum. Syst.

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An electromyogram is a recording of muscle activity. These signals have been used both for medical diagnosis and engineering such as finger motion detection in healthy people and rehabilitation patients. Many studies have been conducted to map the relationship between electromyogram and finger movements, one of which is the relationship between the number of channels used and the complexity of the system. The number of channels used is directly proportional to the complexity of a system. The more complex the system, the heavier the data processing is so that it requires greater resources. Therefore, this study focuses on the construction of a classification system for human finger movements using fewer channels. The number of channels used in this study is 4. Root Mean Square is applied in a sliding window as feature extraction. The classifier used is the artificial neural network. System validation is done with 10-fold cross-validation. The test results of the average accuracy value for the thumb, index finger, middle finger, ring finger, little finger, grip, and relaxation were 89%, 90%, 93%, 95%, 93%, 94%, and 91% respectively which can be said to be quite good considering the number of channels relatively few compared to previous studies.

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“…Salah satu metode ekstraksi ciri adalah dengan menggunakan nilai RMS pada sinyal biomedis seperti EMG dan EEG [14]. Nilai RMS merupakan nilai yang menunjukkan besar power sinyal.…”

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Klasifikasi Gerakan Jari Tangan Berdasarkan Sinyal Electromyogram Pada Lengan

Atmaji

Santoso

Hujja

et al. 2021

Indonesian J. Electron. Instrum. Syst.

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“…Sumber sinyal BCI salah satunya dapat dihasilkan dengan melakukan fokus pada gambar tertentu (Visual), seperti yang telah diterapkan pada kuis matematika sederhana dengan opsi jawaban yang berwarna sehingga subjek dapat menjawab kuis tersebut dengan fokus pada salah satu opsi jawaban yang memiliki warna yang berbeda dan kanal yang digunakan adalah AF3, AF4, F3, F4, F7, F8, O1 dan O2 dengan jendela waktu satu detik untuk klasifikasi dua kelas menghasilkan ratarata akurasi sebesar 75,98% [6]. Visualisasi objek tak berwarna dengan latar belakang warna menghasilkan ciri yang lebih baik dengan akurasi sebesar 63,98% disbanding dengan visualisasi objek tak berwarna tanpa latar belakang warna dengan akurasi sebesar 56,45% [7]. Lampu LED dengan variasi kedipan yang berbeda (7 Hz, 9 Hz, 11 Hz dan 15 Hz) digunakan sebagai stimulus atau biasa disebut dengan sistem BCI berbasis SSVEP (Steady State Visual Evoked Potential) dengan implementasi berupa kendali gerak robot humanoid dan kanal yang digunakan adalah O1 dan O2 dengan jendela waktu lima detik untuk klasifikasi empat kelas menghasilkan rata-rata akurasi sebesar 75% [8].…”

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Kendali Arah pada Brain Computer Interface Berbasis Steady State Visual Evoked Potentials

Maji

Atmaji²

2020

Indonesian J. Electron. Instrum. Syst.

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Various studies regarding to Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) system with Electroencephalogram (EEG) signal has developed as BCI implementation on directional control, however lackness found on those studies which are long time on classification duration, to many electrode channels used and the electrode channels located on special area. This study we developed the SSVEP based BCI system with one second classification duration, four active channels used and electrode channels located based on The International 10-20 System. Stimulus used are red colored object with 11 Hz frequency rate represents as left directional control class, blue colored object with 13 Hz frequency rate represents as right directional control class and white colored background represents as relax class. Filter bank with eight frequency range (11 Hz, 22 Hz, 33 Hz, 13 Hz, 26 Hz, 39 Hz, 12-29 Hz dan 30-50 Hz) followed by Root Mean Square (RMS) used as feature extraction for every second of data. Artificial Neural Network (ANN) classification and 5-Fold Cross Validation are used to knowing the performance of the developed system. Developed BCI system resulted accuracy 78,20% with True Positive Rate (TPR) 86,00% and False Discovery Rate (FDR) 23,21%.

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