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Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete

Minho Won, Hassan Albalawi, Xin Li

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 9, 2015

Summary

This study presents a low-power hardware design for brain-computer interfaces using efficient feature extraction. The novel approach significantly reduces energy consumption for movement decoding applications.

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Area of Science:

Biomedical Engineering
Computer Engineering
Neuroscience

Background:

Brain-computer interfaces (BCIs) enable communication and control by decoding neural signals.
Efficient hardware implementations are crucial for real-time BCI applications, especially for wearable or implantable devices.
Movement decoding from electrocorticography (ECoG) signals is a key BCI application.

Purpose of the Study:

To propose a low-power hardware implementation for movement decoding in brain-computer interfaces.
To introduce novel methods for efficient feature extraction and hardware architecture for discrete cosine transform (DCT).

Main Methods:

Developed an efficient feature extraction method using reduced-resolution discrete cosine transform (DCT).
Designed a novel dual look-up table hardware architecture to perform DCT without explicit multiplication.
Validated the hardware implementation on a Xilinx FPGA Zynq-7000 board for electrocorticography (ECoG) signal decoding.

Main Results:

Achieved significant energy reduction compared to traditional methods.
Demonstrated over 56× energy reduction compared to a reference design using band-pass filters for feature extraction.
Successfully validated the hardware for movement decoding using ECoG signals.

Conclusions:

The proposed low-power hardware implementation offers a significant advancement in energy efficiency for BCI systems.
The novel DCT-based feature extraction and hardware architecture are effective for movement decoding.
This work paves the way for more practical and sustainable BCI applications.