| Title: | Analyzing EEG signal with Machine Learning in Python : graduation thesis |
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| Authors: | ID Siljanovska, Evgenija (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... 
ID Zorjan, Saša (Comentor) |
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| Files: |  UN_Siljanovska_Evgenija_2023.pdf (2,66 MB)
MD5: 5F94F1FD13EDE15102FA296A2180A1CA
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| Language: | English |
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| Work type: | Bachelor thesis/paper |
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| Typology: | 2.11 - Undergraduate Thesis |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | This thesis presents a comprehensive analysis of EEG data using Python libraries, MNE and machine learning techniques. The thesis focuses on utilizing these tools to extract valuable insights from EEG recordings. Our dataset consists of EEG data in the BrainVision format, acquired during a psychology experiment. The analysis involves preprocessing, filtering, segmentation, and visualization of the EEG data. Additionally, machine learning algorithms are employed to classify and predict patterns within the EEG signals. The findings showcase the effectiveness of Python, MNE, and machine learning in EEG analysis. |
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| Keywords: | EEG data, MNE, Machine learning, Analyzing |
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| Place of publishing: | Maribor |
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| Place of performance: | Maribor |
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| Publisher: | [E. Siljanovska] |
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| Year of publishing: | 2023 |
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| Number of pages: | 1 spletni vir (1 datoteka PDF (XIV, 37 f.)) |
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| PID: | 20.500.12556/DKUM-84640 |
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| UDC: | 004.85:616.831-073.7-71(043.2) |
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| COBISS.SI-ID: | 177021699 |
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| Publication date in DKUM: | 17.08.2023 |
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| Views: | 819 |
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| Downloads: | 91 |
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| Metadata: |    |
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| Categories: | KTFMB - FERI
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