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Decoding Electrocorticography Signals by Deep Learning for Brain-Computer Interface
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Deep learning-baserad avkodning av elektrokortikografiska signaler för ett hjärn-datorsgränssnitt (Swedish)
Abstract [en]

Brain-Computer Interface (BCI) offers the opportunity to paralyzed patients to control their movements without any neuromuscular activity. Signal processing of neuronal activity enables to decode movement intentions. Ability for patient to control an effector is closely linked to this decoding performance. In this study, I tackle a recent way to decode neuronal activity: Deep learning. The study is based on public data extracted by Schalk et al. for BCI Competition IV. Electrocorticogram (ECoG) data from three epileptic patients were recorded. During the experiment setup, the team asked subjects to move their fingers and recorded finger movements thanks to a data glove. An artificial neural network (ANN) was built based on a common BCI feature extraction pipeline made of successive convolutional layers. This network firstly mimics a spatial filtering with a spatial reduction of sources. Then, it realizes a time-frequency analysis and performs a log power extraction of the band-pass filtered signals. The first investigation was on the optimization of the network. Then, the same architecture was used on each subject and the decoding performances were computed for a 6-class classification. I especially investigated the spatial and temporal filtering. Finally, a preliminary study was conducted on prediction of finger movement. This study demonstrated that deep learning could be an effective way to decode brain signal. For 6-class classification, results stressed similar performances as traditional decoding algorithm. As spatial or temporal weights after training are slightly described in the literature, we especially worked on interpretation of weights after training. The spatial weight study demonstrated that the network is able to select specific ECoG channels notified in the literature as the most informative. Moreover, the network is able to converge to the same spatial solution, independently to the initialization. Finally, a preliminary study was conducted on prediction of movement position and gives encouraging results.

Place, publisher, year, edition, pages
2019. , p. 45
Series
TRITA-CBH-GRU ; 2019:007
Keywords [en]
Deep learning, BCI, ECoG, Spatial filtering
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-243903OAI: oai:DiVA.org:kth-243903DiVA, id: diva2:1287389
External cooperation
CEA / LETI / CLINATEC
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Supervisors
Examiners
Available from: 2019-02-14 Created: 2019-02-11 Last updated: 2019-02-14Bibliographically approved

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