TY - GEN
T1 - Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography data
AU - Kim, Taehoon
AU - Artan, N. Sertac
AU - Viventi, Jonathan
AU - Chao, H. Jonathan
PY - 2012
Y1 - 2012
N2 - High-resolution Electrocorticography (HR-ECoG) has emerged as a key strategic technology for recording localized neural activity with high temporal and spatial resolution with potential applications in brain-computer interfaces (BCI), and seizure detection for epilepsy. However, HR-ECoG has 400 times the resolution of conventional ECoG, making it a challenge to process, transmit and store the HR-ECoG data. Therefore, simple and efficient compression algorithms are vital for the feasibility of implantable wireless medical devices for HR-ECoG recordings. In this paper, following the observation that HR-ECoG signals have both high spatial and temporal correlations similar to video/image signals, various compression methods suitable for video/image- compression based on motion estimation, discrete cosine transform (DCT) and discrete wavelet transform (DWT)- are investigated for compressing HR-ECoG data. We first simplify these methods to satisfy the low-power requirements for implantable devices. Then, we demonstrate that spatiotemporal compression methods produce up to 46% more data reduction on HR-ECoG data than compression methods using only spatial compression do. We further show that this data reduction can be achieved with low hardware complexity. In particular, among the methods investigated, spatiotemporal compression using DCT-based methods provide the best trade-off between hardware complexity and compression performance, and thus we conclude that DCT-based compression is a promising solution for ultralow-power implantable devices for HR-ECoG.
AB - High-resolution Electrocorticography (HR-ECoG) has emerged as a key strategic technology for recording localized neural activity with high temporal and spatial resolution with potential applications in brain-computer interfaces (BCI), and seizure detection for epilepsy. However, HR-ECoG has 400 times the resolution of conventional ECoG, making it a challenge to process, transmit and store the HR-ECoG data. Therefore, simple and efficient compression algorithms are vital for the feasibility of implantable wireless medical devices for HR-ECoG recordings. In this paper, following the observation that HR-ECoG signals have both high spatial and temporal correlations similar to video/image signals, various compression methods suitable for video/image- compression based on motion estimation, discrete cosine transform (DCT) and discrete wavelet transform (DWT)- are investigated for compressing HR-ECoG data. We first simplify these methods to satisfy the low-power requirements for implantable devices. Then, we demonstrate that spatiotemporal compression methods produce up to 46% more data reduction on HR-ECoG data than compression methods using only spatial compression do. We further show that this data reduction can be achieved with low hardware complexity. In particular, among the methods investigated, spatiotemporal compression using DCT-based methods provide the best trade-off between hardware complexity and compression performance, and thus we conclude that DCT-based compression is a promising solution for ultralow-power implantable devices for HR-ECoG.
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U2 - 10.1109/EMBC.2012.6346105
DO - 10.1109/EMBC.2012.6346105
M3 - Conference contribution
AN - SCOPUS:84870842868
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1012
EP - 1015
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -