Student projects

• Dual-site closed-loop TMS project involving data acquisition on healthy volunteers and EEG data analysis

  
  The aim of this master project is to acquire data and to develop a standardized EEG processing pipeline that will be ran onto healthy participants and stroke patients datasets. After classical EEG pre-processing, the pipeline should include EEG feature extraction tools. Typical features that we want to explore are spatial features, spectral (frequential) features, temporal features (ERP, Time Domain Parameters), connectivity features (coherence, phase locking-values or Directed Transfer Function) or complexity features (entropy, predictive complexity…) and Source level analysis (Source level activation, Spectral granger causality). This will enable extracting EEG biomarkers associated with visual performance changes. Later on, these features could eventually be used for real-time classification algorithms enabling future closed-loop brain stimulation protocols.
  
  Please send your CV and a 300 words statement of your motivation to Estelle Raffin or Michele Bevilacqua

To have your project included in the Master projects database, please contact Felix Naef and Suzanne Balharry.

• MOTUS: Implementation of a 2D pose estimation toolkit to assess the kinematics of individual finger movements

Marker-less pose estimation allows to analyze movement kinematics in participants or patients performing experimental tasks or rehabilitation. Here, we want to develop a pipeline to track movement kinematics while healthy participants and patients perform motor tasks in the laboratory. The main aim of this semester’s project is to implement a 2D pose estimation toolkit to track the poses of participants’ hands during a motor task. Python libraries such as Deeplabcut (http://www.mackenziemathislab.org/deeplabcut) or RTMPose (https://github.com/open-mmlab/mmpose/blob/main/projects/rtmpose/README.md) will be implemented to analyze the kinematics of the hand during the task. Hence, the semester project will have two major aims 1) Integration of the camera system in the current experimental set-ups (incl. generating triggers to epoch the video data, synchronize multiple cameras etc.); 2) Extract finger kinematics from previously acquired videos.

Please send your CV and a 300 words statement of your motivation to Estelle Raffin or Thomas Paul

• MOTUS: Causal evidence of the neuronal pathways subserving reversal learning

  The aim of the project is to disentangle the causal role of the primary motor cortex and the sensorimotor thalamus in the ability to learn new rules based on reinforcement signals. This study involves the combination functional MRI during the task and Transcranial Ultrasound Stimulation (TUS), which will be used to disturb neural processing in the two regions of interest. Changes in behavioural performances and in brain activity will be measured. The student will perform TUS-fMRI acquisition under the supervision of a trained investigator, and will analyse the behavioral/fMRI data.
  
  Please send your CV and a 300 words statement of your motivation to Estelle Raffin or Thomas Paul