Throughout the years, technology has shown a strong inclination and curiosity in exploring its capabilities in the realm of human activity classification. They have developed a wide range of innovations, including wearable devices and software for artificial intelligence systems. Individuals interested in utilising and developing intelligent systems are already immersed in the realm of neural network and machine learning algorithms. They are actively investigating the potential of these algorithms to classify a wide range of human activities, including bodily movements, hand gestures, and even subtle actions like eye movements.
An increasingly popular device in the market is the wireless electroencephalogram (EEG) headset. An EEG is a device utilised to detect and interpret electrical signals generated by the brain during various activities, encompassing physical, mental, and emotional processes. Primarily employed in the medical domain, particularly in the past, the utilisation of this particular sensor's structure is now being investigated in diverse fields due to the current advancements in technology.
Your objective is to develop a highly efficient Machine Learning model using data collected from EEGs. This model should accurately classify and distinguish between two states of minute action: when a person's eyes are closed and when they are open.
The dataset consists of a total of 15 attributes, each of which includes the eye state classification for each data point. These data points were acquired using a video recording that was conducted simultaneously with the collection of EEG data using the EEG device.
The dataset contains the attributes which correspond to the channels of the EEG device that was used to record the data. The instances were input in chronological order, facilitating the analysis of a valuable test set for syntactic and semantic theories.
A total of 14 electrodes are affixed to specific areas of the scalp, with each electrode being labelled and placed on the following regions: AF3, F7, F3, FC5, T7, P7, FC6, F4, F8, O1, O2, P8, T8, and AF4.


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