Brain-Computer Interface-Based Abstract Art Interior Space Color Concept Control System

This framework describes how to build a brain‑computer interface (BCI) that turns a person’s thoughts and feelings into changing colors for abstract art or interior design. The system records brain activity with electroencephalography (EEG) and looks for the neural rhythms that appear when people judge beauty or feel emotions. To clean and analyze the EEG data, it uses: – band‑pass filtering, – independent component analysis (ICA) to remove artifacts, and – short‑time Fourier transform (STFT) to get spectral power in the alpha, beta, and theta bands. The extracted power values are linked to color attributes—hue, saturation, and brightness—by either a multi‑class support vector machine (SVM) or a deep neural network

classifier. The classifier’s output (the identified emotional state) feeds an adaptive control algorithm that continuously updates the color scheme of a virtual room or a digital abstract artwork. The system’s performance is judged by its classification accuracy, latency, and user‑satisfaction scores. Overall, this approach gives a clear roadmap for creating closed‑loop bio‑feedback systems that can make architectural spaces and interactive art respond directly to a person’s brain activity.

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