Difference between revisions of "BCI-controlled Drone"
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| + | [[Category:Digital]][[Category:Available]][[Category:Semester Thesis]] [[Category:Master Thesis]] [[Category:2019]][[Category:Hot]][[Category:Human Intranet]][[Category:BCI]][[Category:Drone]][[Category:Xiaywang]][[Category:SmartSensors]][[Category:EmbeddedAI]] | ||
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Latest revision as of 15:27, 23 October 2023
Contents
Description
A brain–computer interface (BCI) is a device that enables communication between the human brain and an external device. It aims to recognize the human’s intentions from spatiotemporal neural activity typically recorded by a large set of electroencephalogram (EEG) electrodes. What makes it particularly challenging, however, is its susceptibility to errors in the recognition of human intentions, especially during motor imagery (MI). The underlying reason is the high inter-subject variance, which makes it difficult to build one universal model for all subjects.
In this project, the student works on the acquisition and processing of electroencephalography (EEG) data to control a drone using a Brain-Machine Interface (BMI) with motor imagery (MI). This includes:
- Set up experimental paradigm to acquire EEG data in the motor imagery paradigm
- Record data with of multiple subjects (potentially from our lab)
- Analyse the data and apply a standard classifier [1,2]
- Move forward to online asynchronous BCI experiments
- Potentially control a drone
Status: Available
- Looking for 1-2 students for a semester project or group project.
- Supervision: Xiaying Wang
Prerequisites
- Machine Learning
- Python and C Programming
- Embedded Systems
Character
- 15% Theory
- 45% Implementation
- 40% Testing
Professor
Literature
- [1] M. Hersche, et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features, 2018
- [2] X. Wang, et. al., An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing, 2020
