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Computational techniques are increasingly used in the field of neuroscience. Signal processing and machine learning have promising clinical applications in automating the detection and characterisation of pathological patterns of neural activiy.
One tool that is commonly used to measure neural functions is electroencephalography -EEG-. EEG is a non-invasive technique that measures time-series of electric activity of the brain, though electrodes placed on the scalp. EEG is used as a diagnostic tool in neurological disorders, like epilepsy or coma. EEG recordings in coma patients carry information about the integrity of neural functions in the absence of consciousness, and can be used to predict the patients’ outcome and chances of survival. Recent work from our group has characterised patterns of EEG resposnes to sounds during the first day of coma. We showed that neural synchrony of EEG activity is predictive of patients’ outcome 3 months later, and that neural complexity is indicative of consciousness levels. However, it is not known how the synchrony and complexity of neural responses evolve over time.
This master thesis will use signal processing and machine learning techniques to analyse a rich EEG dataset of coma patients recorded over the first two days of coma. The goal is to evaluate how neural signals of coma patients evolve over time, and establish clinical predictors of their outcome.
The student working on this project will gain experience in signal processing of time series data, machine learning algorithms for biomedical data, and in the development of clinical biomarkers.
For more infomration please contact:
Sigurd Alnes: sigurd.alnes@inf.unibe.ch
Athina Tzovara: athina.tzovara@inf.unibe.ch
When we sleep, we process auditory stimuli from the environment around us. However, the neural mechanisms that allow the human brain to process stimuli during sleep are under-explored. We are collecting single-unit and local field potential (LFP) responses from patients with epilepsy, with intracranially implanted electrodes, with the aim of better characterizing how the auditory response is modulated by sleep.
This project, suitable for a master or bachelors thesis, will primarily focus on spike sorting of single-unit data and analysing firing rates and local field potentials in response to auditory stimuli.
The student working on it will gain experience with processing and analysis of a rich electrophysiological dataset and contribute to expanding the understanding of how information processing is altered by the absence of consciousness. This thesis will give experience with Python/matlab programming, processing and visualizing time-series signals.
For more infomration please contact:
Sigurd Alnes: sigurd.alnes@inf.unibe.ch
Athina Tzovara: athina.tzovara@inf.unibe.ch
When we sleep, we process auditory stimuli from the environment around us. However, the neural mechanisms that allow the human brain to process stimuli during sleep are under-explored. Recent studies suggest that responsiveness to external stimuli in sleep is related to complexity of neural activity and levels of neuronal noise.
Tools from the field of computer science, such as information theory or machine learning can be used to quantify the complexity of neural signals, recorded through electroencephalography (EEG). EEG is a non-invasive technique that measures electric activity of the brain through electrodes on the scalp. Measures of scale-free dynamics of neural activity can be applied on time-series of EEG signals to quantify the complexity of neural responses, or levels of neuronal noise. Interestingly, past studies suggest that the compexity of ongoing neural activity is modulated by sleep. However, it remains unknown how changes in neural complexity during sleep affect sensory processing of external stimuli (e.g. sounds).
The focus of this thesis will be on using techniques from information theory to study complexity of EEG responses to sounds during sleep and wakefulness. The student working on this project will analyse a dataset of high-density sleep EEG recordings using signal processing and information theory techniques. Also, the student will gain experience with Python programming, processing and visualizing time-series signals. No previous experience with sleep or EEG is necessary.
For more infomration please contact:
Sigurd Alnes: sigurd.alnes@inf.unibe.ch
Athina Tzovara: athina.tzovara@inf.unibe.ch