DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
This work addresses efficiency challenges in EEG-based brain-computer interfaces for real-time applications, though it appears incremental as it builds on existing genetic algorithm and channel selection techniques.
The paper tackles the problem of high computational and hardware costs in brain-computer interfaces by proposing DGAFF, a channel selection method that combines sequential search with a genetic algorithm to accelerate convergence and improve performance, outperforming other methods in motor imagery classification on their dataset.
Brain-computer interface systems aim to facilitate human-computer interactions in a great deal by direct translation of brain signals for computers. Recently, using many electrodes has caused better performance in these systems. However, increasing the number of recorded electrodes leads to additional time, hardware, and computational costs besides undesired complications of the recording process. Channel selection has been utilized to decrease data dimension and eliminate irrelevant channels while reducing the noise effects. Furthermore, the technique lowers the time and computational costs in real-time applications. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep GA Fitness Formation (DGAFF). The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance. The system evaluation is based on a lightweight deep neural network that automates the whole model training process. The proposed method outperforms other channel selection methods in classifying motor imagery on the utilized dataset.