We describe our work using linear discrimination of multi-channel electroencephalography for single-trial detection of neural signatures of visual recognition events. We demonstrate the approach as a methodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection. We then show how the approach can be utilized to construct a novel type of brain-computer interface, which we term cortically-coupled computer vision. In this application, a large database of images is triaged using the detected neural signatures. We show how `corticaltriaging ' improves image search over a strictly behavioral response
Abstract—Noninvasive electroencephalogram (EEG) record-ings provide for easy and safe access to huma...
The human brain achieves visual object recognition through multiple stages of linear and nonlinear t...
BACKGROUND Deep learning has revolutionized the field of computer vision, where convolutional neu...
We describe our work using linear discrimination of multi-channel electroencephalography for single...
Abstract We describe a real-time EEG-based brain-computer interface (BCI) system for triaging image...
<p>A series of fusion techniques are developed and applied to EEG and pupillary recording analysis i...
Cortical spatiotemporal signal patterns based on object recognition can be discerned from visual sti...
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies ...
In this thesis, inspired by the development of the Brain-computer-interface (BCI) technology, we pre...
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies ...
Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on a...
Abstract — The timing of a behavioral response, such as a button press in reaction to a visual stimu...
In this paper we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical...
We present results from single-trial analyses conducted on Electroencephalography (EEG) data recorde...
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies...
Abstract—Noninvasive electroencephalogram (EEG) record-ings provide for easy and safe access to huma...
The human brain achieves visual object recognition through multiple stages of linear and nonlinear t...
BACKGROUND Deep learning has revolutionized the field of computer vision, where convolutional neu...
We describe our work using linear discrimination of multi-channel electroencephalography for single...
Abstract We describe a real-time EEG-based brain-computer interface (BCI) system for triaging image...
<p>A series of fusion techniques are developed and applied to EEG and pupillary recording analysis i...
Cortical spatiotemporal signal patterns based on object recognition can be discerned from visual sti...
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies ...
In this thesis, inspired by the development of the Brain-computer-interface (BCI) technology, we pre...
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies ...
Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on a...
Abstract — The timing of a behavioral response, such as a button press in reaction to a visual stimu...
In this paper we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical...
We present results from single-trial analyses conducted on Electroencephalography (EEG) data recorde...
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies...
Abstract—Noninvasive electroencephalogram (EEG) record-ings provide for easy and safe access to huma...
The human brain achieves visual object recognition through multiple stages of linear and nonlinear t...
BACKGROUND Deep learning has revolutionized the field of computer vision, where convolutional neu...