Ulster University

Researchers have added a dedicated readout neuron that outputs decision confidence to an echo state network, a neuro-inspired computational model suited to time-series data, so that confidence is updated continuously as a decision forms. In a simulated decision task, confidence activity rose as stimuli became stronger and easier to distinguish. In EEG-based motor imagery classification, confidence activity increased with decision accuracy and distinguished correct from incorrect decisions, especially in better-performing participants. The study is a preprint and has not been peer reviewed.

A preprint proposes a two-stage CNN-LSTM-RL framework that uses reinforcement learning to apply residual kinematic corrections to the output of a CNN-LSTM continuous motor imagery decoder. The RL agent is trained offline, without direct EEG input, to optimize motion accuracy against a target trajectory. Compared with CNN-LSTM alone, CNN-LSTM-RL lifted the 2D mean correlation from 0.5076 to 0.7181 and the VR-scene correlation from 0.6420 to 0.7780, cutting RMSE by 40.2% and 38.2% respectively. The authors say correcting kinematic error through offline residual RL strengthens 3D BCI motor imagery decoding without additional neural data; the study has not been peer reviewed.