A zero-shot learning approach to the development of brain-computer interfaces for image retrieval


Autoři: Ben McCartney aff001;  Jesus Martinez-del-Rincon aff001;  Barry Devereux aff001;  Brian Murphy aff001
Působiště autorů: Queen’s University Belfast, United Kingdom aff001;  BrainWaveBank Ltd. Belfast, United Kingdom aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0214342

Souhrn

Brain decoding—the process of inferring a person’s momentary cognitive state from their brain activity—has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature selection methods, and which maps EEG activity to biologically inspired computer vision and linguistic models. We apply this approach to solve the problem of identifying viewed images from recorded brain activity in a reliable and scalable way. We demonstrate competitive decoding accuracies across two EEG datasets, using a zero-shot learning framework more applicable to real-world image retrieval than traditional classification techniques.

Klíčová slova:

Research and analysis methods – Bioassays and physiological analysis – Electrophysiological techniques – Brain electrophysiology – Electroencephalography – Imaging techniques – Computer imaging – Biology and life sciences – Physiology – Electrophysiology – Neurophysiology – Neuroscience – Brain mapping – Functional magnetic resonance imaging – Neuroimaging – Sensory perception – Vision – Cognitive science – Cognitive psychology – Learning – Learning and memory – Psychology – Medicine and health sciences – Clinical medicine – Clinical neurophysiology – Diagnostic medicine – Diagnostic radiology – Magnetic resonance imaging – Radiology and imaging – Computer and information sciences – Software engineering – Preprocessing – Computer vision – Engineering and technology – Social sciences – Linguistics – Semantics


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Článek vyšel v časopise

PLOS One


2019 Číslo 9

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