Emotion Recognition Classification Using an EEG- based Brain Computer Interface System based on Different Machine Learning Models

Brain Computer Interface (BCI) is a communication channel between brain and machine which helps to control the instruction over the machine. The BCI helps in both the situations where patients are not able to move a bit and utter a word. There are many devices made that are being used by the people with disabilities and helping them in doing their work. Brain computer interfaces have been made useful for the patients with mental disorders like strokes and also for the other physical disorders. BCI first acquires and analyzes the brain Electroencephalography (EEG) signals, translating them into the desired actions that are needed to fulfill the command given by the brain. The recent advancement in the brain computer interface technology is exciting for the scientists, engineers and clinical persons which further enlighten the rapid development and growing research. The classification and detection on BCI systems for the patients have been made. This study proposes a novel stochastic model that classifies the emotions as: positive, negative and neutral with the accuracy of 96% using the ensemble model (Multiclass Logistic Regression, Light Gradient Boosting Machine (LGBM), Random Forest Classifier and Decision Tree Classifier).