Classification of living and non-living objects from MEG recordings

Author

Mapelli, I. ; Ozkurt, T.E.

Author_Institution

Saglik Bilisimi Bolumu, Orta Dogu Teknik Univ., Ankara, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

3

Abstract

The mapping of brain areas involved in the representation of living vs. non-living objects has been matter for debate. Electroencephalography (EEG) and magnetoencephalography (MEG) recordings combined with advanced machine learning techniques have been useful for this purpose. This study conducted analysis on features extracted from MEG recordings of two subjects performing a language task. Mean accuracies of 57.68% for visual task (chance level 50%) and 52.52% for auditory task (chance level 50%) on decoding living vs. non-living category and 49.39% on decoding auditory living vs. auditory non-living vs. visual living vs. visual non-living category (chance level 25%) were obtained.

Keywords

brain models; electroencephalography; feature extraction; learning (artificial intelligence); magnetoencephalography; medical signal processing; EEG; MEG recording; auditory living; auditory task; electroencephalography; feature extraction; language task; living object classification; machine learning; magnetoencephalography; visual living; visual task; Brain; Decoding; Electroencephalography; Feature extraction; Magnetic recording; Magnetoencephalography; Visualization; Classification; EEG; Feature extraction; MEG; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531544

Filename

6531544