Dasar - Dasar Electroencephalography (EEG) bagi Riset Psikologi

Khakim, Zulfikri; Kusrohmaniah, Sri

doi:10.22146/buletinpsikologi.52328

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…Flask is used as the core and framework of the application. By using Flask and the Python language, developers can create APIs that are structured easily and do not require a particular tool or library to use [20]. This study used Python Flask to preprocess EEG data and run a trained deep-learning model for classifying EEG signals in ASD and normal patients.…”

Section: Model Deployment Using Flaskmentioning

confidence: 99%

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Melinda,

Arnia,

Yafi

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Autism spectrum disorder (ASD) is a disorder of the nervous system from birth and during infancy. This disorder affects children's development, making it difficult for nerve function to develop, and causes the child concerned to have difficulty in fostering social relationships. Early detection of children with ASD is needed so that treatment is fast and on target. Currently, facilities and research on early diagnosis of ASD patients through EEG signals are still very few, requiring much cost and more effort to analyze EEG signals in examinations related to ASD detection cases. This study proposes a mobile phone application that can distinguish people living with ASD and normal data signals based on asynchronous EEG brain signals. This research also produces a preprocessing algorithm and BCI2000 EEG data signal so that it can be automated using Python. This research also produces an output model, namely the Deep Learning Convolutional Neural Network, which is deployed using Python-Flask so that the diagnosis of EEG signals with ASD and normal patients can be used on various platforms through restAPI. This research is also expected to help the community and support the diagnosis of ASD sufferers so that they can be handled appropriately. Data for ASD sufferers and normal data were correctly classified into the appropriate class. Handling this disease requires close and integrated cooperation, so this ASD classification will be very helpful for patients and can make a diagnosis in a faster time, enabling patients to receive targeted treatment and therapy.

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Section: Model Deployment Using Flaskmentioning

confidence: 99%

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Melinda,

Arnia,

Yafi

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The essence of feature extraction is to find certain patterns or features from data for further analysis, in the context of EEG these features are associated with behavior, mental processes, or brain activity. whereas in the context of EMG it is associated with muscle strength There are various ways to perform feature extraction [28]. The feature extraction method used in the selected articles can be classified into time-domain and frequency-domain.…”

Section: Content Analysismentioning

confidence: 99%

“…The Power Spectrum Density approach, which is used in the article's frequency domain feature extraction, is a research technique [33,34,35],Band Power technique [36,24,37]. The analytical method that combines time-domain and frequency-domain research is known as time-frequency analysis [28]. The article uses the Wavelet approach [38,39,22,40,41] and the Fast Fourier Transform method [42,43,44,6,34] for feature extraction via time-frequency analysis.…”

Section: Content Analysismentioning

confidence: 99%

Literature Review: Impact of Noise on Cognitive Performance Using Electroencephalography

Astuti

Suhardi

Laksono

et al. 2023

AMM

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One of the environmental factors that can influence the onset of fatigue is noise exposure. Besides noise has a negative impact on everything from cognitive processes to mental and physical health. Noise's impact can be measured using brain activity and the waves it generates. The main goal of this article is to review how electroencephalography can be used to determine the impact of noise on cognitive performance. The method is based on bibliometric analysis stages, including literature study from the Scopus database and the snowball search method. The findings revealed that of the 30 articles examined, 28 used the experimental design method with continuous noise, and 10 used the time-frequency domain for feature extraction. According to the results of the study, there seems to be an increase in the use of EEG for performance measurement, such as mental fatigue, mental workload, working memory, attention, stress, and the presence of noise factors that influence cognitive performance.

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“…EEG devices, which can measure brain electrical activity, were initially used in research related to brain disorders. However, due to its non-invasive nature and the growing need for accurate psychological measurements, EEG is also being used in research related to broader cognitive functions [7].…”

Section: Introductionsmentioning

confidence: 99%

The Relationship between Self-Regulated Learning (SRL) and Cognitive Function with The Performance of qEEG Examination in First Year Medical Students

Dewi,

Digna,

Makkiyah

et al. 2024

JPS

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Introductions: Medical students are required to lifelong learning. Electroencephalog-raphy is a neuroscience tool that can be used to record brain wave activity related to a person’s cognitive function. Objective: This study was conducted to see the relationship between self-regulated learning and cognitive function with quantitative electroencepha-logram (qEEG) parameters. Methods: This study used an observa-tional analytic approach to assess self-regulated learning and cog-nitive function using the MSLQ and MoCA-INA questionnaires. qEEG recording to see brain wave activity was done when the eyes were open and closed and seen in alpha and beta waves. The sam-ple of this study amounted to 32 respondents who were willing to carry out each research procedure. Results: The study findings reveal a significant correlation between SRL and qEEG parameters. In the open-eye condition, such a correla-tion exists in the frontal and central regions with regard to alpha waves (P = 0.046 and P = 0.047). In contrast, in the closed-eye condition, it exists in the occipital, central, and parietal regions with alpha waves (P = 0.005, P = 0.021, and P = 0.049) and the central region with beta waves (P = 0.030). There is a significant correla-tion between cognitive function and qEEG parameters observed in both open and closed-eye conditions within the frontal region beta waves (P = 0.024, P = 0.044, and P = 0.021). Conclusions: There is a relationship between SRL and cognitive function with alpha and beta wave activity on qEEG recording.

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Dasar - Dasar Electroencephalography (EEG) bagi Riset Psikologi

Cited by 5 publications

References 42 publications

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Literature Review: Impact of Noise on Cognitive Performance Using Electroencephalography

The Relationship between Self-Regulated Learning (SRL) and Cognitive Function with The Performance of qEEG Examination in First Year Medical Students

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