University of Twente Student Theses
Channel reduction for EEG recordings of nociceptive evoked brain activity
Poelarends, R.J. (2022) Channel reduction for EEG recordings of nociceptive evoked brain activity.
This is the latest version of this item.
|
PDF
4MB |
| Abstract: | EEG is a useful method to assess sensory deficits in chronic pain patients. One of the barriers for widescale use of EEG in a clinical context is the required time and training for applying a 32+ channel EEG cap. In this work, it is investigated whether the number of EEG channels can be reduced to four without losing important nociceptive evoked brain activity. Artifact removal from EEG recordings is a major challenge in a few-channel scenario, since most existing techniques cannot guarantee efficient and effective artifact removal performance. Consequently, I propose Noise-Assisted Fast Multivariate Empirical Mode Decomposition combined with Canonical Correlation Analysis (NA-FMEMD-CCA) to remove EMG and EOG artifacts from the preselected T7, Cz, T8 and Fz EEG channels. The performance of the proposed method is compared with the currently used artifact removal method, Independent Component Analysis (ICA), and evaluated using power spectral density, visual signal-inspection and deep learning. The results demonstrate that the four preselected channels contain the important nociceptive evoked brain activity. The proposed method removes all eye-movement artifacts, but is yet unable to remove very intense spiking eye-blink artifacts and EMG artifacts properly. In addition, NA-FMEMD-CCA currently removes parts of important nociceptive evoked brain activity content. Parameter tuning is required to optimise the artifact removal method performance and preserve all nociceptive evoked brain activity content. Therefore, it can be concluded that four-channel EEG can be used in order to make EEG more applicable for widescale use in the clinic, yet the artifact removal method requires further investigation to be applicable. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 30 exact sciences in general, 44 medicine |
| Programme: | Biomedical Technology BSc (56226) |
| Link to this item: | https://purl.utwente.nl/essays/90827 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page