Brain computer interaction provides a direct communication channel between human brains and external devices by translating human intentions into control signals. Brain-Computer Interface (BCI) systems can be developed via a variety of brain signal acquisition techniques such as electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), optical imaging (i.e., functional Near InfraRed (fNIR), etc. EEG signals is one of the most widely used brain signal modality for BCIs due to their non-invasiveness, high time resolution, ease of acquisition, and cost effectiveness compared to other brain signal recording methods.
To the best of our knowledge, there are no studies that tries to passively find out whether a person is enjoying a particular genre of videos, specifically from brain signals. To do this, the very first thing that one should be able to classify is, which type of video a person is watching. With the vision of building a BCI based video recommender system, we are experimenting with several state-of-the-art algorithms for each of the submodules (pre-processing, feature extraction, feature selection and classification) of the Signal Processing module of a BCI system in order to find out what combination of algorithms best predicts what type of video a person is watching.
AK Mutasim, MK Islam and MA Amin conceived and planned the experiments. AK Mutasim, RS Tipu and MR Bashar collected data, carried out the experiments, simulations and contributed to sample preparation. All authors contributed to the interpretation of the results. AK Mutasim wrote the manuscripts with support from RS Tipu, MR Bashar and MK Islam. All authors provided critical feedback and helped shape the research, analysis and manuscript. MK Islam and MA Amin supervised the project.