Saggese, Gerardo (2023) Spike Detector Algorithms and Approximating Hardware Arithmetic Circuits: Enhancing Efficiency in Brain-Machine Interface Systems. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Spike Detector Algorithms and Approximating Hardware Arithmetic Circuits: Enhancing Efficiency in Brain-Machine Interface Systems |
| Autori: | Autore Email Saggese, Gerardo gerardo.saggese@unina.it |
| Data: | 13 Dicembre 2023 |
| Numero di pagine: | 184 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Russo, Stefano stefano.russo@unina.it |
| Tutor: | nome email Strollo, Antonio Giuseppe Maria [non definito] |
| Data: | 13 Dicembre 2023 |
| Numero di pagine: | 184 |
| Parole chiave: | brain-machine interface; spike detector; approximate computing; low-power; approximate multipliers |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/01 - Elettronica |
| Depositato il: | 18 Dic 2023 09:54 |
| Ultima modifica: | 12 Ago 2026 05:36 |
| URI: | https://www.fedoa.unina.it/id/eprint/15628 |
Abstract
Brain-Machine Interface (BMI) systems have gained attention for direct communication between brain and outside environment. The spike detection algorithm is crucial for extracting neural information from recorded signals. Integrating spike detection algorithms with proximity calculations improves the efficiency of BMI systems and enables real-time processing for responsive device control. This reduction in computational intensity and power consumption promotes low-power, energy-efficient BMI hardware. The main objective of my research activity is to overcome the challenges faced by conventional spike detection algorithms, especially in terms of computational intensity and power consumption, when applied to implantable BMI systems with a large number of channels. To this end, spike detection algorithms tailored to BMI applications have been researched and developed, and their performance evaluated using metrics such as accuracy, computational effort and resource requirements. Another research topic that builds on this foundation is the approximate computation paradigm. Multipliers are essential building blocks in many signal processing tasks, including spike detection algorithms. Therefore, I have been working on developing approximate multipliers to reduce the complexity and computational cost of multiplication operations while maintaining an acceptable level of accuracy, resulting in improved computational efficiency and reduced power consumption. The use of approximate multipliers in spike detection algorithms can improve the overall efficiency and performance of the spike detector and thus the BMI system. Overall, my research aims to advance the field of BMI by addressing the computational challenges associated with spike detection algorithms and exploring the benefits of approximate computational techniques. The results of my research have the potential to provide valuable insights into optimising computational resources, power efficiency and real-time processing capabilities, paving the way for more efficient and practical BMI systems.
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