title: Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform.
abstract: This paper describes a low-power hardware implementation for movement decoding of brain computer interface. Our proposed hardware design is facilitated by two novel ideas: (i) an efficient feature extraction method based on reduced-resolution discrete cosine transform (DCT), and (ii) a new hardware architecture of dual look-up table to perform discrete cosine transform without explicit multiplication. The proposed hardware implementation has been validated for movement decoding of electrocorticography (ECoG) signal by using a Xilinx FPGA Zynq-7000 board. It achieves more than 56× energy reduction over a reference design using band-pass filters for feature extraction.
authors.lastname: Minho Won
authors.forename: Hassan
authors.initials: H
authors.lastname: Albalawi
authors.lastname: Xin Li
authors.forename: Donald E
authors.initials: DE
authors.lastname: Thomas
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003201:Computers; D011211:Electric Power Supplies; D000069280:Electrocorticography; D004867:Equipment Design; D006801:Humans; D009068:Movement