Abstract:
Over the past few years, machine learning has enabled organizations to develop insights
into the psychological aspects of consumer decision-making to enhance their business.
In the context of brain-computer interfaces (BCI), one of our research goals is to profile
players by invoking emotional responses through video games genres and recording the
players’ EEG. It will further enable us to classify emotions over a spectrum representing
true states of a user in a decision-making context and capture those emotions to
understand specific personality characteristics.
Savitzky-Golay filter has been used to clean the non-stationary, non-linear, and extremely
noisy signal and recommend it for single-channel EEG devices like Neurosky
Mindwave Mobile 2. Sixteen features from time, frequency, time-frequency domains
and classified emotions using each domain separately and as a combination called hybrid.
SVM, K-NN, and Boosted Trees classifiers have been used where the highest
accuracy achieved is 82.26% with Boosted Trees classifier. Our findings propagate
that only four emotions (happy, bored, relaxed, stressed) can be classified for a singlechannel
EEG device. Two emotions, happy and bored, achieved the highest individual
accuracy of 90.01% and 88.76%, respectively.
Keywords – EEG, affective computing, machine learning, neuropsychology, video games,
emotions recognition, personality-traits.
