TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Online learning in neural decoding using incremental linear discriminant analysis

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017
KustantajaIEEE
Sivut173-177
Sivumäärä5
ISBN (elektroninen)9781538631942
DOI - pysyväislinkit
TilaJulkaistu - 19 tammikuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Cyborg and Bionic Systems - Beijing, Kiina
Kesto: 17 lokakuuta 201719 lokakuuta 2017

Conference

ConferenceIEEE International Conference on Cyborg and Bionic Systems
MaaKiina
KaupunkiBeijing
Ajanjakso17/10/1719/10/17

Tiivistelmä

Neural decoding focuses on predicting behavior variables based on neural activities. Linear discriminant analysis (LDA) has been successfully used in pattern recognition and machine learning to find the set of discriminant vectors to characterize two or more classes of objects. However, LDA cannot be directly used for real-time neural decoding problems. In this paper, we propose an incremental LDA with online learning method to overcome this limitation. The dataflow techniques are implemented in the LIDE (LIghtweight Dataflow Environment), which provides capabilities to systematically optimize and integrate embedded software components for signal and information processing. Using these techniques along with online learning, an efficient real-time neural decoding system can be attained.

Tutkimusalat

Julkaisufoorumi-taso