RESUMEN
In the present study, we investigated traveling waves induced by transcranial alternating current stimulation in the alpha frequency band of healthy subjects. Electroencephalographic data were recorded in 12 healthy subjects before, during, and after phase-shifted stimulation with a device combining both electroencephalographic and stimulation capacities. In addition, we analyzed the results of numerical simulations and compared them to the results of identical analysis on real EEG data. The results of numerical simulations indicate that imposed transcranial alternating current stimulation induces a rotating electric field. The direction of waves induced by stimulation was observed more often during at least 30 s after the end of stimulation, demonstrating the presence of aftereffects of the stimulation. Results suggest that the proposed approach could be used to modulate the interaction between distant areas of the cortex. Non-invasive transcranial alternating current stimulation can be used to facilitate the propagation of circulating waves at a particular frequency and in a controlled direction. The results presented open new opportunities for developing innovative and personalized transcranial alternating current stimulation protocols to treat various neurological disorders. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09997-1.
RESUMEN
Introduction: Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands. Methods: The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using L p -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using L p with p = 0., 0.5, and 1. Results: The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA. Discussion: The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.