“…Artifact from subject such as body movement, EMG or ECG can be reduces by conducting proper EEG procedure. But artifact from technical such as 50-60 Hz power line noise can only be removed by filtering [8] [15].…”
Section: Signal Processingmentioning
confidence: 99%
“…Notch filter was use to remove the 50 Hz power line noise [8] [15].In addition, software such as Savitzky-Golay [2], Matlab algorithm [7][8], Independent Component Analysis, ICA [14] [16] can be used to remove the artifact in brainwave signal. Furthermore, the metric in the filter data can be calculated in each of the electrode.…”
Section: Signal Processingmentioning
confidence: 99%
“…Power specral density, PSD is the most common feature use in the analysis data of EEG [7][8][9][10] [24]. PSD can be obtain from the extraction using welch or hanning window and Fast Fouries Transformer, FFT [3] [9].…”
Section: Signal Processingmentioning
confidence: 99%
“…The total number of subject for this research is 20 people, consist of 10 subjects of healthy person and 10 subjects of stroke patient. The healthy person is act as control subject for the comparison data, aged between 18 to 50 years old, medically fit, and no history of any neurological disease, seizures or psychiatric disorder [2][5] [8]. For the stroke patient, they are the subject that suffer from stroke and admitted to a rehabilitation centre [1].The stroke patient is the post-stroke for 3-15 month, paralyzed on lower part of the body and able to perform the FES-assisted exercised and EEG procedure [1][2].…”
This paper presents a conceptual of EEG analysis and classification of brainwaves signal for alpha and beta signals during Functional Electrical Stimulation, FES-assisted exercise. The characteristics of brainwave signals, data acquisition for electroencephalograph (EEG) signal and data session are identified. This paper also includes the criteria of the subject for both stroke patient and healthy person. The process of filtering the artifact and sampling the data were studied based on the established previous worked. In addition, a review on feature extraction for further classifying of brainwave signals stroke patients before and after performing FES-assisted exercised were also identified.
“…Artifact from subject such as body movement, EMG or ECG can be reduces by conducting proper EEG procedure. But artifact from technical such as 50-60 Hz power line noise can only be removed by filtering [8] [15].…”
Section: Signal Processingmentioning
confidence: 99%
“…Notch filter was use to remove the 50 Hz power line noise [8] [15].In addition, software such as Savitzky-Golay [2], Matlab algorithm [7][8], Independent Component Analysis, ICA [14] [16] can be used to remove the artifact in brainwave signal. Furthermore, the metric in the filter data can be calculated in each of the electrode.…”
Section: Signal Processingmentioning
confidence: 99%
“…Power specral density, PSD is the most common feature use in the analysis data of EEG [7][8][9][10] [24]. PSD can be obtain from the extraction using welch or hanning window and Fast Fouries Transformer, FFT [3] [9].…”
Section: Signal Processingmentioning
confidence: 99%
“…The total number of subject for this research is 20 people, consist of 10 subjects of healthy person and 10 subjects of stroke patient. The healthy person is act as control subject for the comparison data, aged between 18 to 50 years old, medically fit, and no history of any neurological disease, seizures or psychiatric disorder [2][5] [8]. For the stroke patient, they are the subject that suffer from stroke and admitted to a rehabilitation centre [1].The stroke patient is the post-stroke for 3-15 month, paralyzed on lower part of the body and able to perform the FES-assisted exercised and EEG procedure [1][2].…”
This paper presents a conceptual of EEG analysis and classification of brainwaves signal for alpha and beta signals during Functional Electrical Stimulation, FES-assisted exercise. The characteristics of brainwave signals, data acquisition for electroencephalograph (EEG) signal and data session are identified. This paper also includes the criteria of the subject for both stroke patient and healthy person. The process of filtering the artifact and sampling the data were studied based on the established previous worked. In addition, a review on feature extraction for further classifying of brainwave signals stroke patients before and after performing FES-assisted exercised were also identified.
“…Pandriad et al assessed the impact of biofeedback and neurofeedback training on smokers working on smoking cessation projects, exploring possible correlations between subjective mood scores and training performance [16]. Murat et al developed the brainwave balancing index (BBI), which uses the subject's ability to think and work as direct indicators to generate vast opportunities for improving human potential [17]. Cevat Unal et al suggested that gamma activity was significantly higher on the right side of the brain compared to the left side [18], and Sümeyra Altan et al suggested that neurofeedback can be used to restore sympathovagal imbalances.…”
His research interests include cybersecurity and data mining. DONGMIN SHIN received a B.S. and Ph.D. degree in Computer Engineering from Sejong University, Seoul, Korea, in 2011 and 2016, respectively. He is a co-founder and principal Engineer at the WEDA company. His research interests include natural user interface, human computer interaction, and data mining.
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