Advanced EEG-Based Neuroadaptive Systems for Gamified Cognitive Training and Therapy

Neuroadaptive systems such as those based on EEG have great potential for cognitive and rehabilitative training. However, there are issues of scalability, noise and when it comes to different types of users they do not perform well. To address these limitations, this study proposes the highly innovative EEG-based neuroadaptive system that incorporates signal processing; deep learning; and adaptive gamified tools. This, in fact, forms the core of the proposed framework that employs empirical mode decomposition (EMD) and the discrete wavelet transform (DWT) for noise reduction and feature extraction in combination with convolutional and recurrent neural networks for automated, high-accuracy classification. He also used assistive technologies - LightSword and PATIENT- therefore; real time EEG feedback was used to adapt cognitive training tasks for example relaxation, concentration and memory training. The noise level efficiency of this experiment was 93.7%, the classifier correctly identified the data with 95.3% efficiency, and there was 27% enhancement in attention span and 21% reduction in stress level. Conducted with reference to different demographic characteristics and in the presence of extraneous noise, reliability testing further supported the flexibility of the proposed system. These results highlight the usefulness of the proposed framework for enhancing the effectiveness of EEG-based cognitive training with relevance to therapeutic, educational, and rehabilitation practices in the future. The studies done so far warrant further enhancement of its applicability, which can be studied by future research on multi-modal data integration and longitudinal studies.