EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain-machine interfaces (MI-BMIs) based on electroencephalography(EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power consumption by processing the data locally. In this paper, we propose EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters. Its low memory footprint and low computational complexity for inference make it suitable for embedded classification on resource-limited devices at the edge. Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77.35% classification accuracy in 4-class MI. By finding the optimal network hyperparameters per subject, we further improve the accuracy to 83.84%. Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG datasets with MI experiments. The results indicate that EEG-TCNet successfully generalizes beyond one single dataset, outperforming the current state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.Comment: 8 pages, 6 figures, 5 table

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks

Dietary Phenylalanine Requirement of Fingerling Oreochromis Niloticus (Linnaeus)

This study was conducted to determine the dietary phenylalanine for fingerling Oreochromis niloticus by conducting an 8 weeks experiment in a flow-through system (1-1.5L/min) at 28°C water temperature. Phenylalanine requirement was determined by feeding six casein-gelatin based amino acid test diets (350 g kg– 1 CP; 16.72 kJ g–1 GE) with graded levels of phenylalanine (4, 6.5, 9, 11.5, 14 and 16.5 g kg–1 dry diet) at a constant level (10 g kg–1) of dietary tyrosine to triplicate groups of fish (1.65±0.09 g) near to satiation. Absolute weight gain (AWG g fish-1), feed conversion ratio (FCR), protein deposition (PD%), phenylalanine retention efficiency (PRE%) and RNA/DNA ratio was found to improve with the increasing concentrations of phenylalanine and peaked at 11.5 g kg–1 of dry diet. Quadratic regression analysis of AWG, PD and PRE against varying levels of dietary phenylalanine indicated the requirement at 12.1, 11.6, and 12.7 g kg–1 dry diet, respectively and the inclusion of phenylalanine at 12.1 g kg–1 of dry diet, corresponding to 34.6 g kg–1 dietary protein is optimum for this fish. Based on above data, total aromatic amino acid requirement of fingerling O. niloticus was found to be 20.6 g kg–1 (12.1 g kg–1 phenylalanine+8.5 g kg–1 tyrosine) of dry diet, corresponding to 58.8 g kg–1 of dietary protein