Arlisa Wulandari

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About

Arlisa Wulandari is a researcher at the forefront of affective brain-computer interfaces (BCI), specializing in the intersection of emotion recognition and neural signal processing. Her work centers on decoding human emotional states from electroencephalography (EEG) signals to enable intuitive, emotion-driven control of external devices. In her most-cited paper, "Emotion brain-computer interface using wavelet and recurrent neural networks" (2020, 13 citations), Wulandari proposed a pioneering framework that extracts wavelet-based features from EEG data and feeds them into a recurrent neural network (RNN) to classify three distinct emotions within a five-second window. This system was successfully used to control a robot simulator, demonstrating a practical pathway from neural activity to real-world action. Her contribution lies in bridging the gap between raw brain signals and meaningful, real-time interaction—a key challenge in BCI research. By integrating wavelet transforms with deep learning, Wulandari has advanced the accuracy and responsiveness of emotion-based control systems. Her work holds promise for assistive technologies, neurorehabilitation, and human-robot interaction, marking her as an emerging voice in the field of neural engineering.