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EEG resolutions in detecting and decoding finger movements from spectral analysis.

Xiao R, Ding L - Front Neurosci (2015)

Bottom Line: These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms.Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%).The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, University of Oklahoma Norman, OK, USA.

ABSTRACT
Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g., hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.

No MeSH data available.


Accuracies in detecting movements from resting using combined features. **p < 0.01.
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Figure 5: Accuracies in detecting movements from resting using combined features. **p < 0.01.

Mentions: The top three bars in Figure 5 present the decoding accuracy using combined features from only one category of spectral features (projection weights on spectral PCs or PSDs). It is observed that two or three spectral PCs together produce significantly higher decoding accuracy, i.e., 90 and 91%, respectively, than individual PCs (p < 0.05 for the 1st PC and p < 0.0005 for the 2nd and 3rd PCs, Table 1). Similar phenomenon is also observed for the combined alpha and beta bands feature, in which the decoding accuracy (i.e., 75.6%) is significantly higher than the feature only from either alpha or beta band alone (p < 0.05, Table 1). Moreover, the combined features from the spectral PCs as the input feature for classification show much higher accuracy than the combined PSD features (p < 0.001, Table 1). On the other hand, when features from different categories are combined (spectral PCs and PSDs), only slight improvements in decoding accuracy are observed (91.5% by combining total five features), which are not significantly different from ones obtained through the use of combined spectral PCs (i.e., 91% for combined three PCs).


EEG resolutions in detecting and decoding finger movements from spectral analysis.

Xiao R, Ding L - Front Neurosci (2015)

Accuracies in detecting movements from resting using combined features. **p < 0.01.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555046&req=5

Figure 5: Accuracies in detecting movements from resting using combined features. **p < 0.01.
Mentions: The top three bars in Figure 5 present the decoding accuracy using combined features from only one category of spectral features (projection weights on spectral PCs or PSDs). It is observed that two or three spectral PCs together produce significantly higher decoding accuracy, i.e., 90 and 91%, respectively, than individual PCs (p < 0.05 for the 1st PC and p < 0.0005 for the 2nd and 3rd PCs, Table 1). Similar phenomenon is also observed for the combined alpha and beta bands feature, in which the decoding accuracy (i.e., 75.6%) is significantly higher than the feature only from either alpha or beta band alone (p < 0.05, Table 1). Moreover, the combined features from the spectral PCs as the input feature for classification show much higher accuracy than the combined PSD features (p < 0.001, Table 1). On the other hand, when features from different categories are combined (spectral PCs and PSDs), only slight improvements in decoding accuracy are observed (91.5% by combining total five features), which are not significantly different from ones obtained through the use of combined spectral PCs (i.e., 91% for combined three PCs).

Bottom Line: These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms.Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%).The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, University of Oklahoma Norman, OK, USA.

ABSTRACT
Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g., hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.

No MeSH data available.