Emotion significantly impacts human cognition, influencing learning, memory, and decision-making. Although software-based methods have advanced emotion recognition, they lack biological plausibility and fail to capture the interplay between emotional valence and arousal. In this work, a dual-path memristive circuit is proposed to represent emotional states along two dimensions: valence and arousal. EEG \(\beta \) -band signals from the F3 and F4 channels are extracted and normalized to 0–1 V as circuit inputs. The valence pathway is designed based on the Brain Emotional Learning (BEL) framework, while the arousal pathway is constructed with reference to the Long Short-Term Memory (LSTM) model. Volatile and non-volatile memristors are employed to emulate short-term and long-term synaptic dynamics, respectively. The generated valence and arousal voltages are mapped onto a 2-D emotional state space, enabling the identification of four affective states: Happy, Sad, Calm, and Terrible. A classification accuracy of 83.3% is achieved from the DEAP dataset. All circuit evaluations are validated through PSpice simulations, with real EEG recordings from the DEAP dataset serving as input stimuli. The circuit is further integrated with a fuzzy inference system for real-time emotional feedback, demonstrating its applicability in wearable affective computing and companion robotics.
