TY - GEN
T1 - Restoring Latency-Variable ERP Components from Single Trials
T2 - 5th Fifth International Conference on Cognitive Neurodynamics, ICCN 2015
AU - Ouyang, Guang
AU - Sommer, Werner
AU - Zhou, Changsong
N1 - This work was partially supported by Hong Kong Baptist University (HKBU) Strategic Development Fund, the HKBU Faculty Research Grant (FRG2/13-14/022), the Hong Kong Research Grant Council (RGC) (HKBU202710) and Germany-Hong Kong Joint Research Scheme (G-HK012/12), the National Natural Science Foundation of China (Grant No. 11275027) to G.O. and C.Z., and the Germany-Hong Kong Joint Research Scheme (PPP 56062391) to W.S. This research was conducted using the resources of the High Performance Cluster Computing Centre, Hong Kong Baptist University, which receives funding from RGC, University Grant Committee of the HKSAR and HKBU.
Publisher Copyright:
© 2016 Springer Science+Business Media Singapore
PY - 2016/1/30
Y1 - 2016/1/30
N2 - Electroencephalography (EEG) is widely used in cognitive neuroscience as a brain signal with high temporal resolution. Strong latency variability pervades cognitive EEG responses across single trials, but is not taken into consideration by the conventional averaging method yielding event-related potentials (ERPs). This trial-to-trial variability may strongly smear and mix ERP components and diminish their amplitudes, impeding proper identification of the spatiotemporal representation of brain activities reflecting specific cognitive subprocesses. Furthermore, rich dynamic information about single trials is lost in averaged ERPs. Here we propose a model of ERPs as consisting of temporally overlapping components locked to different external events or varying in latency from trial to trial as a foundation for a new ERP decomposition and reconstruction method, residue iteration decomposition (RIDE). RIDE obtains latency-corrected waveforms and topography of the components, and retrieves the latencies and amplitudes of the separated components in single trials. RIDE was tested with real data and provides new perspectives for investigating brain–behavior relationships using EEG data in latency-corrected reconstructed ERPs, separated components, and information about variability in single trials.
AB - Electroencephalography (EEG) is widely used in cognitive neuroscience as a brain signal with high temporal resolution. Strong latency variability pervades cognitive EEG responses across single trials, but is not taken into consideration by the conventional averaging method yielding event-related potentials (ERPs). This trial-to-trial variability may strongly smear and mix ERP components and diminish their amplitudes, impeding proper identification of the spatiotemporal representation of brain activities reflecting specific cognitive subprocesses. Furthermore, rich dynamic information about single trials is lost in averaged ERPs. Here we propose a model of ERPs as consisting of temporally overlapping components locked to different external events or varying in latency from trial to trial as a foundation for a new ERP decomposition and reconstruction method, residue iteration decomposition (RIDE). RIDE obtains latency-corrected waveforms and topography of the components, and retrieves the latencies and amplitudes of the separated components in single trials. RIDE was tested with real data and provides new perspectives for investigating brain–behavior relationships using EEG data in latency-corrected reconstructed ERPs, separated components, and information about variability in single trials.
U2 - 10.1007/978-981-10-0207-6_70
DO - 10.1007/978-981-10-0207-6_70
M3 - Conference proceeding
SN - 9789811002052
SN - 9789811091049
T3 - Advances in Cognitive Neurodynamics
SP - 519
EP - 525
BT - Advances in Cognitive Neurodynamics (V)
A2 - Wang, Rubin
A2 - Pan, Xiaochuan
PB - Springer
CY - Singapore
Y2 - 3 June 2015 through 7 June 2015
ER -