arXiv:2603.18078v2 Announce Type: replace Abstract: We present the Variational Phasor Circuit (VPC), a deterministic classical learning architecture on the continuous $S^1$ unit-circle manifold. Inspired by variational quantum circuits, VPC replaces dense weight matrices with trainable phase shifts, local unitary mixing, and structured interference in the ambient complex space, giving a unified method for binary and multi-class classification of spatially distributed signals. We evaluate VPC on real motor-imagery electroencephalography (EEG) from the PhysioNet Motor Movement/Imagery database (
The continuous advancements in AI and neuroscience, coupled with the increasing demand for more efficient and robust brain-computer interfaces, drive innovation in this field.
This development presents a novel approach to Brain-Computer Interface (BCI) classification, potentially leading to more accurate and reliable control systems, expanding accessibility and application for individuals with disabilities.
The introduction of Variational Phasor Circuits offers a new computational paradigm for processing brain signals, moving away from traditional dense weight matrices towards phase-native architectures for enhanced classification.
- · BCI developers
- · Medical technology companies
- · Individuals with motor impairments
- · Neuroscience researchers
- · Outdated BCI classification methods
- · Traditional signal processing techniques for EEG
Improved performance and robustness of brain-computer interfaces for various applications.
Accelerated development of assistive technologies and real-time neural control systems.
Potential for new paradigms in human-computer interaction and augmentation based on direct brain signal interpretation.
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