Design of an intelligent reminder system based on EEG monitoring

EEG waves reflect the synchronized firing of neurons in the brain, and different waveform patterns are associated with different cognitive states, such as attention, relaxation, or sleep. In this paper, we design an intelligent reminder system based on EEG monitoring. The process of system involves several steps, including pre-processing, filtering, artifact removal, feature extraction, and classification. Preprocessing involves removing any artifacts or noise from the raw EEG signal. Filtering is used to remove unwanted frequencies or amplify desired frequencies. Artifact removal techniques include independent component analysis and template matching, which can identify and remove artifacts such as eye blinks or heartbeats. Feature extraction algorithms aim to extract significant information from the processed signal. Classification algorithms are then used to classify the extracted features into specific categories, such as cognitive states, diseases, or mental tasks. Commonly used classification algorithms include support vector machines, neural networks, and linear discriminant analysis. EEG collection can potentially have an effect on reminding fatigue driving, as it may increase drivers' awareness of their cognitive states and alert them to signs of fatigue. By monitoring the electrical activity of the brain, EEG can provide real-time feedback on changes in cognitive states, such as decreased attention or increased drowsiness.