Abstract
| Language | English |
|---|---|
| Pages | 2207-2210 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 26 Aug 2015 |
| Event | 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 25 Aug 2015 → 29 Aug 2015 |
Conference
| Conference | 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 |
|---|---|
| Country | Italy |
| City | Milan |
| Period | 25/08/15 → 29/08/15 |
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Keywords
- electroencephalogram signals
- network analysis
- working memory representation
- visual short-term memory (VSTM) tasks
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Comparison of network analysis approaches on EEG connectivity in beta during visual short-term memory binding tasks. / Smith, Keith; Azami, Hamed; Escudero, Javier; Parra, Mario A.; Starr, John M.
2015. 2207-2210 Paper presented at 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy.Research output: Contribution to conference › Paper
TY - CONF
T1 - Comparison of network analysis approaches on EEG connectivity in beta during visual short-term memory binding tasks
AU - Smith, Keith
AU - Azami, Hamed
AU - Escudero, Javier
AU - Parra, Mario A.
AU - Starr, John M.
PY - 2015/8/26
Y1 - 2015/8/26
N2 - We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term Memory (VSTM) tasks which have proven useful in the assessment of clinical and preclinical Alzheimer’s disease. We compare network analysis using Maximum Spanning Trees (MSTs) with network analysis obtained using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising method of network analysis negating the more classical use of thresholds which are so far chosen arbitrarily. However, we find that the threshold analyses outperforms MSTs for detection of functional network differences. Particularly, MSTs fail to find any significant differences. Further, the thresholds detect significant differences between shape and shape-colour binding tasks when these are tested in the left side of the display screen, but no such differences are detected when these tasks are tested for in the right side of the display screen. This provides evidence that contralateral activity is a significant factor in sensitivity for detection of cognitive task differences.
AB - We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term Memory (VSTM) tasks which have proven useful in the assessment of clinical and preclinical Alzheimer’s disease. We compare network analysis using Maximum Spanning Trees (MSTs) with network analysis obtained using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising method of network analysis negating the more classical use of thresholds which are so far chosen arbitrarily. However, we find that the threshold analyses outperforms MSTs for detection of functional network differences. Particularly, MSTs fail to find any significant differences. Further, the thresholds detect significant differences between shape and shape-colour binding tasks when these are tested in the left side of the display screen, but no such differences are detected when these tasks are tested for in the right side of the display screen. This provides evidence that contralateral activity is a significant factor in sensitivity for detection of cognitive task differences.
KW - electroencephalogram signals
KW - network analysis
KW - working memory representation
KW - visual short-term memory (VSTM) tasks
U2 - 10.1109/EMBC.2015.7318829
DO - 10.1109/EMBC.2015.7318829
M3 - Paper
SP - 2207
EP - 2210
ER -