Comparison of LSTM- and GRU-Type RNN Networks for Attention and Meditation Prediction on Raw EEG Data from Low-Cost Headsets

Abstract

This study bridges neuroscience and artificial intelligence by developing advanced models to predict cognitive states—specifically attention and meditation—using raw EEG data collected from low-cost commercial devices such as NeuroSky and Brainlink. Leveraging the temporal capabilities of recurrent neural networks (RNNs), particularly long short-term memory (LSTM) and gated recurrent units (GRUs), the study evaluates their effectiveness in predicting future cognitive states. These predictions have applications in real-time brain–computer interface (BCI) systems, enhancing responsiveness and adaptability in dynamic environments like robotic control. The proposed LSTM model demonstrated superior predictive accuracy for meditation states, achieving a Root Mean Squared Error (RMSE) of 10.90, while the GRU model excelled in predicting attention states, with an RMSE of 11.79. Both models outperformed the results provided by the proprietary eSense algorithm, reinforcing the potential of raw EEG data in cognitive-state analysis. Notably, inference times were optimized to under 50 milliseconds, making the models suitable for real-time applications. These findings underline the feasibility of using raw EEG signals from affordable devices for robust real-time prediction, offering a significant step forward in applied neuroscience. This research lays the groundwork for further exploration of RNN architectures in BCI applications, enabling safer, more intuitive, and personalized interactions in assistive technologies and beyond.