Abstract
EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous EEG signals. In this framework, it was suggested that EEG patterns can be better detected with EEG data transformed with Surface Laplacian computation (SL) than with the unprocessed raw potentials. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and these obtained with the SL computed from 26 electrodes (full resolution SL). More importantly, satisfactorily recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%). These results demonstrated also the utility of linear classifiers for the detection of mental patterns in the BCI field.
| Original language | English |
|---|---|
| Pages (from-to) | 323-328 |
| Number of pages | 6 |
| Journal | Medical Engineering and Physics |
| Volume | 23 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2001 |
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Keywords
- Brain Computer Interface
- Linear classifiers
- Signal Space Projection
- Surface Laplacian
ASJC Scopus subject areas
- Orthopedics and Sports Medicine
- Psychology(all)
Cite this
Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers. / Babiloni, Fabio; Cincotti, Febo; Bianchi, Luigi; Pirri, Giada; Del R. Millán, José; Mouriño, Josep; Salinari, Serenella; Grazia Marciani, Maria.
In: Medical Engineering and Physics, Vol. 23, No. 5, 2001, p. 323-328.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers
AU - Babiloni, Fabio
AU - Cincotti, Febo
AU - Bianchi, Luigi
AU - Pirri, Giada
AU - Del R. Millán, José
AU - Mouriño, Josep
AU - Salinari, Serenella
AU - Grazia Marciani, Maria
PY - 2001
Y1 - 2001
N2 - EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous EEG signals. In this framework, it was suggested that EEG patterns can be better detected with EEG data transformed with Surface Laplacian computation (SL) than with the unprocessed raw potentials. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and these obtained with the SL computed from 26 electrodes (full resolution SL). More importantly, satisfactorily recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%). These results demonstrated also the utility of linear classifiers for the detection of mental patterns in the BCI field.
AB - EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous EEG signals. In this framework, it was suggested that EEG patterns can be better detected with EEG data transformed with Surface Laplacian computation (SL) than with the unprocessed raw potentials. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and these obtained with the SL computed from 26 electrodes (full resolution SL). More importantly, satisfactorily recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%). These results demonstrated also the utility of linear classifiers for the detection of mental patterns in the BCI field.
KW - Brain Computer Interface
KW - Linear classifiers
KW - Signal Space Projection
KW - Surface Laplacian
UR - http://www.scopus.com/inward/record.url?scp=0034955692&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0034955692&partnerID=8YFLogxK
U2 - 10.1016/S1350-4533(01)00049-2
DO - 10.1016/S1350-4533(01)00049-2
M3 - Article
C2 - 11435146
AN - SCOPUS:0034955692
VL - 23
SP - 323
EP - 328
JO - Medical Engineering and Physics
JF - Medical Engineering and Physics
SN - 1350-4533
IS - 5
ER -