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EnglishExtracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency.
| Titolo: | Compressed sensing based seizure detection for an ultra low power multi-core architecture |
| Autore/i: | Aghazadeh, Roghayeh; Montagna, Fabio; Benatti, Simone; Rossi, Davide; Frounchi, Javad |
| Autore/i Unibo: | |
| Anno: | 2018 |
| Titolo del libro: | PROCEEDINGS 2018 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING & SIMULATION (HPCS 2018) |
| Pagina iniziale: | 492 |
| Pagina finale: | 495 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/HPCS.2018.00083 |
| Abstract: | Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency. |
| Data stato definitivo: | 4-apr-2019 |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipo | Licenza | |
|---|---|---|---|---|
| CS.pdf | Postprint | Licenza per accesso libero gratuito | Open Access Visualizza/Apri |
http://hdl.handle.net/11585/652250
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