La electroencefalografía permite la decodificación continua de los ángulos de movimiento de la mano en coordenadas...

Hand movements in task space are typically represented using either Cartesian or polar coordinate systems. While Cartesian coordinates are commonly used in electroencephalography (EEG)-based brain-computer interface (BCI) studies, polar coordinates offer a more natural representation for circular motion by directly encoding angular information. This study investigates the feasibility of continuous decoding of hand motion angles in polar coordinates using EEG signals. In the paradigm, human participants engaged in bimanual circular tracing with a fixed radius while their EEG signals were recorded. To evaluate the feasibility of this approach, 6 deep learning models, including commonly used EEGNet, DeepConvNet, and ShallowConvNet, and their variants incorporating long short-term memory (LSTM) layers, were employed. Performance was assessed using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CC) between decoded and actual angles. Across 8 participants, all 6 models significantly outperformed the chance level (P < 0.01), with the best model achieving an MSE of 1.012 rad², an MAE of 0.627 rad, and a CC of 0.895. These results demonstrate the feasibility of continuous angular decoding of circular hand motion in polar coordinates using EEG signals. This approach offers a promising alternative to traditional Cartesian-based decoding methods, particularly for applications involving circular or rotational movements.