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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.


Spectral profiles of the 3 PCs from all subjects obtained by PCA. Red, green, and blue curves represent profiles of the 1st, 2nd, and 3rd PCs, respectively.
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Figure 1: Spectral profiles of the 3 PCs from all subjects obtained by PCA. Red, green, and blue curves represent profiles of the 1st, 2nd, and 3rd PCs, respectively.

Mentions: Figure 1 depicts the profiles of first three PCs from the spectral PCA analysis, with each curve representing spectral structure derived from one subject in all plots. It is noted that all curves in each PC present similar patterns, suggesting the consistency of these spectral structures across subjects, while different PCs show distinct profiles along the whole frequency range (i.e., 1–70 Hz). The 1st PC (red curves) is generally flat with positive elevations across the whole frequency range, which reveals a broadband phenomenon. The 2nd PC (green curves) presents spectral peaks at both alpha and beta bands while exhibits values close to zero for other frequency bands. The 3rd PC (blue curves) presents main peaks at the alpha band.


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

Xiao R, Ding L - Front Neurosci (2015)

Spectral profiles of the 3 PCs from all subjects obtained by PCA. Red, green, and blue curves represent profiles of the 1st, 2nd, and 3rd PCs, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Spectral profiles of the 3 PCs from all subjects obtained by PCA. Red, green, and blue curves represent profiles of the 1st, 2nd, and 3rd PCs, respectively.
Mentions: Figure 1 depicts the profiles of first three PCs from the spectral PCA analysis, with each curve representing spectral structure derived from one subject in all plots. It is noted that all curves in each PC present similar patterns, suggesting the consistency of these spectral structures across subjects, while different PCs show distinct profiles along the whole frequency range (i.e., 1–70 Hz). The 1st PC (red curves) is generally flat with positive elevations across the whole frequency range, which reveals a broadband phenomenon. The 2nd PC (green curves) presents spectral peaks at both alpha and beta bands while exhibits values close to zero for other frequency bands. The 3rd PC (blue curves) presents main peaks at the alpha band.

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.