write-an-abstract-about-optimizing-motion-trajectory-prediction-by-enhancing-EEG-data

This study aims to optimize motion trajectory prediction by enhancing electroencephalogram (EEG) data. EEG signals were recorded from participants while they performed a reaching task. The recorded signals were then processed using wavelet analysis and independent component analysis (ICA) to extract features that can be used to predict motion trajectory. Machine learning algorithms were used to train models on the extracted features and predict the trajectory of the reaching task. The results showed that enhancing the EEG data with wavelet analysis and ICA improved the accuracy of trajectory prediction, compared to using raw EEG data. This study provides a promising approach for optimizing motion trajectory prediction and has potential applications in the field of neuroprosthetics and human-robot interaction.