An offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials

Kook, Hyunseok; Gupta, Lalit; Kota, Srinivas; Molfese, Dennis L.; Lyytinen, Heikki

doi:10.1016/j.patcog.2007.09.001

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…EEG epochs that had a kurtosis value higher than four were considered to contain artifact and were excluded (Lin et al, 2017). To further eliminate artifacts, a median based artifact detection algorithm was used (Kook, Gupta, Kota, Molfese, & Lyytinen, 2008). With this approach, the median signal of the remaining EEG epochs was calculated, and a Euclidean distance from each epoch to the median signal was then computed.…”

Section: Eeg Acquisition and Preprocessingmentioning

confidence: 99%

Hippocampal Theta Oscillations Support Successful Associative Memory Formation

2020

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Models of memory formation posit that recollection as compared to familiarity-based memory depends critically on the hippocampus, which binds features of an event to its context. For this reason, the contrast between study items that are later recollected versus those that are recognized on the basis of familiarity should reveal electrophysiological patterns in the hippocampus selectively involved in associative memory encoding. Extensive data from studies in rodents support a model in which theta oscillations fulfill this role, but results in humans results have not been as clear. Here, we employed an associative recognition memory procedure to identify hippocampal correlates of successful associative memory encoding and retrieval in patients undergoing intracranial EEG monitoring. We identified a dissociation between 2-5 Hz and 5-9 Hz theta oscillations, by which 2-5 Hz oscillations uniquely were linked with successful associative memory in both the anterior and posterior hippocampus. These oscillations exhibited a significant phase reset that also predicted successful associative encoding, distinguished recollected from familiar items at retrieval, and contributed to reinstatement of encoding-related patterns that distinguished these items. Our results provide direct electrophysiological evidence that 2-5 Hz hippocampal theta oscillations support the encoding and retrieval of memories based on recollection but not familiarity. 2. Significance Statement Extensive fMRI evidence suggests that the hippocampus plays a selective role in recollection rather than familiarity, during both encoding and retrieval. However, there is little or no electrophysiological evidence that speaks to whether the hippocampus is selectively involved in recollection. Here, we used intracranial EEG from human participants engaged in an associative recognition paradigm. The findings suggest that oscillatory power and phase reset in the hippocampus are selectively associated with recollection rather than familiarity-based memory judgements. Furthermore, reinstatement of oscillatory patterns in the hippocampus was stronger for successful recollection than familiarity. Collectively, the findings support a role for hippocampal theta oscillations in human episodic memory.

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Section: Eeg Acquisition and Preprocessingmentioning

confidence: 99%

Hippocampal Theta Oscillations Support Successful Associative Memory Formation

2020

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“…We applied an offline noise detection algorithm to filter out noise and possible interictal activity. 32 EEG data were sampled at 500Hz. We analyzed two classes of retrieval events in our analysis: correctly retrieved items and PLIs.…”

Section: Retrieval Data Analysismentioning

confidence: 99%

Direct brain recordings identify hippocampal and cortical networks that distinguish successful versus failed episodic memory retrieval

2020

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Human data collected using noninvasive imaging techniques have established the importance of parietal regions towards episodic memory retrieval, including the angular gyrus and posterior cingulate cortex. Such regions comprise part of a putative core episodic retrieval network. In free recall, comparisons between contextually appropriate and inappropriate recall events (i.e. prior list intrusions) provide the opportunity to study memory retrieval networks supporting veridical recall, and existing findings predict that differences in electrical activity in these brain regions should be identified according to the accuracy of recall. However, prior iEEG studies, utilizing principally subdural grid electrodes, have not fully characterized brain activity in parietal regions during memory retrieval and have not examined connectivity between core recollection areas and the hippocampus or prefrontal cortex. Here, we employed a data set obtained from 100 human patients implanted with stereo EEG electrodes for seizure mapping purposes as they performed a free recall task. This data set allowed us to separately analyze activity in midline versus lateral parietal brain regions, and in anterior versus posterior hippocampus, to identify areas in which retrieval-related activity predicted the recollection of a correct versus an incorrect memory. With the wide coverage afforded by the stereo EEG approach, we were also able to examine interregional connectivity. Our key findings were that differences in gamma band activity in the angular gyrus, precuneus, posterior temporal cortex, and posterior (more than anterior) hippocampus discriminated accurate versus inaccurate recall as well as active retrieval versus memory search. The left angular gyrus exhibited a significant power decrease preceding list intrusions as well as unique phase-amplitude coupling properties, whereas the prefrontal cortex was unique in exhibiting a power increase during list intrusions. Analysis of connectivity revealed significant hemispheric asymmetry, with relatively sparse leftsided functional connections compared to the right hemisphere. One exception to this finding was elevated connectivity between the prefrontal cortex and left angular gyrus. This finding is interpreted as evidence for the engagement of prefrontal cortex in memory monitoring and mnemonic decision-making.

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“…Errors in averaging of small signal samples can be reduced more efficiently by using median rather than mean [1]- [3]. Artifacts in visual evoked potentials caused by eye movement, eye blink, external noise, internal noise of recording instruments, etc., are removed by using different techniques such as blind component separation, multichannel median test, standard deviation etc., [22]and [27].…”

Section: Detection Of Non Responsive Channels and Trials By Mediamentioning

confidence: 99%