1. Introduction to working of brain: This topic will introduce the various brain (anatomical) structures (like frontal, temporal lobes etc) and the functioning of the brain in terms of communication through neurons.
2. Brain neural activity (EEG, ERP): The concepts of Electroencephalogram (EEG) and Event Related Potential (ERP) will be discussed in details as they are the foundation for brain computer interfaces (BCI).
3. Introduction to BCI - technologies, components and types: Various BCI technologies will be discussed including FNIR, TDCS and various stimuli. Further, the components (amplifier, sensor etc) of BCI technologies will be introduced.
4. Recording of brain neural activity: This is the most critical step in BCI as any BCI activity depends on the quality of the data captured from the scalp. Various montages like 10-20 system, references (like ear lobe) and other data capturing steps (like ensuring good contact etc) will be elaborated.
5. Identifying & rectifying artefacts: The data captured from the scalp includes various physiological artefacts (like eye movements etc) as well as non-physiological artefacts (like line noise etc). It will be taught on how to identify and rectify these artefacts.
6. Source localization techniques: It is important to know the origin of source in the brain - where the signal is being produced. Various inverse methods (like LORETA etc) will be introduced to teach the source localization from EEG signals.
7. Feature Extraction for BCI: This topic will introduce EEG data analysis for feature extraction in time domain (like entropy etc), frequency domain (like spectral analysis etc) and time-frequency analysis using wavelet transform.
8. Connectivity for BCI: The concept of brain networks (like resting state network etc) will be introduced and corresponding connectivity measures will be discussed. Both the functional as well as effective connectivity will be taught.
9. Microstates for BCI: The advantage of EEG is its temporal resolution. The method of microstates will be taught that exploits the temporal resolution by finding stable brain states between 30 to 100ms.
10. Using machine learning for BCI: The application of machine learning in BCI will be taught with respect to the various features extracted (like microstates, connectivity etc). In addition, the potential as well as the limitations of deep learning in BCI will be discussed.
11. Clinical (medical) applications of BCI: Various clinical applications (like controlling a wheel chair, moving a prosthetic limb etc) will be introduced during this lecture.
12. Non-Clinical (non-medical) applications of BCI: Various non-clinical applications (like controlling characters in a video game, flying a quadcopter etc) will be introduced.
13. Future of BCI: The final lecture of the course will discuss the latest trends in BCI as well as the future applications of BCI in various fields (like rescue services, aviation, freight etc).

1. Record EEG signals
2. EEGLab Demonstration
3. ERP experiment (AEP, VEP, MMN)
4. Controlling an object

Every student will choose one project from a list of approved projects that are relevant for this course. The implementation, presentation and documentation of the project will be evaluated.