The Joint Use of the Tangential Electric Field and Surface Laplacian in EEG Classification

Carvalhaes, Claudio G.; Barros, J. Acácio de; Perreau-Guimaraes, Marcos; Suppes, Patrick

doi:10.1007/s10548-013-0305-y

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…This combination was grounded on the observation that the surface Laplacian derivation is closely related to the spatial component of the electric field oriented normally to the scalp surface, thus not containing substantial information about features encoded in tangential direction. Not surprisingly, this combination of physically distinct but somewhat supplementary quantities resulted in significant improvement for a variety of classification tasks as described in Carvalhaes et al (2014).…”

Section: Discussionmentioning

confidence: 92%

“…Finally, we want to mention our effort in recent papers to develop a vector form of EEG that combines the surface Laplacian and the tangential components of the electric field projected on the scalp surface (Wang et al, 2012;Carvalhaes et al, 2014). This combination was grounded on the observation that the surface Laplacian derivation is closely related to the spatial component of the electric field oriented normally to the scalp surface, thus not containing substantial information about features encoded in tangential direction.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, from a practical point of view, such points where the derivatives are not well defined consist of a set of measure zero, and therefore of little practical importance. We refer the reader to Carvalhaes et al (2014) for details.…”

Section: Physical Interpretation Of the Surface Laplacianmentioning

confidence: 99%

“…The motivation for the use of the surface Laplacian technique is grounded on Ohm's law. This law establishes a local relationship between the surface Laplacian of scalp potentials and the underlying flow of electric current caused by brain activity (see Appendix in Carvalhaes et al, 2014). Presumably, this local relation should improve spatial resolution, reflecting electrical activity from a more restricted cortical area than what is observed in conventional topography.…”

Section: Introductionmentioning

confidence: 98%

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The surface Laplacian technique in EEG: Theory and methods

Carvalhaes¹,

Barros

2015

International Journal of Psychophysiology

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This paper reviews the method of surface Laplacian differentiation to study EEG. We focus on topics that are helpful for a clear understanding of the underlying concepts and its efficient implementation, which is especially important for EEG researchers unfamiliar with the technique. The popular methods of finite difference and splines are reviewed in detail. The former has the advantage of simplicity and low computational cost, but its estimates are prone to a variety of errors due to discretization. The latter eliminates all issues related to discretization and incorporates a regularization mechanism to reduce spatial noise, but at the cost of increasing mathematical and computational complexity. These and several others issues deserving further development are highlighted, some of which we address to the extent possible. Here we develop a set of discrete approximations for Laplacian estimates at peripheral electrodes and a possible solution to the problem of multiple-frame regularization. We also provide the mathematical details of finite difference approximations that are missing in the literature, and discuss the problem of computational performance, which is particularly important in the context of EEG splines where data sets can be very large. Along this line, the matrix representation of the surface Laplacian operator is carefully discussed and some figures are given illustrating the advantages of this approach. In the final remarks, we briefly sketch a possible way to incorporate finite-size electrodes into Laplacian estimates that could guide further developments.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Physical Interpretation Of the Surface Laplacianmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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The surface Laplacian technique in EEG: Theory and methods

Carvalhaes¹,

Barros

2015

International Journal of Psychophysiology

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An insight into the hardware and software aspects of a BCI system with focus on ultra-low power bulk driven OTA and Gm-C based filter design, and a detailed review of the recent AI/ML techniques

Singh¹,

Gautam²,

Sharan³

2022

Artificial Intelligence-Based Brain-Computer Interface

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A robust beamforming approach for early detection of readiness potential with application to brain-computer interface systems

Mahmoodi

Abadi

Khajepur

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Early detection of intention to move, at self-paced voluntary movements from the activities of neural current sources on the motor cortex, can be an effective approach to brain-computer interface (BCI) systems. Achieving high sensitivity and pre-movement negative latency are important issues for increasing the speed of BCI and other rehabilitation and neurofeedback systems used by disabled and stroke patients and helps enhance their movement abilities. Therefore, developing high-performance extractors or beamformers is a necessary task in this regard. In this paper, for the sake of improving the beamforming performance in well reconstruction of sources of readiness potential, related to hand movement, one kind of surface spatial filter (spherical spline derivative on electrode space) and the linearly constrained minimum variance (LCMV) beamformer are utilized jointly. Moreover, in order to achieve better results, the real head model of each subject was created, using individual head MRI, and was used in beamformer algorithm. Also, few optimizations were done on reconstructed source signal powers to help our template matching classifier with detection of movement onset for five healthy subjects. Our classification results show an average true positive rate (TPR) of 77.1% and 73.1%, false positive rate (FPR) of 28.96% and 28.74% and latency of -512.426 ±396. 7ms and - 360.29 ±252. 16 ms from signals of current sources of motor cortex and sensor space respectively. It can be seen that the proposed method has reliable sensitivity and is faster in prediction of movement onset and more reliable to be used for online BCI in future.

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The Joint Use of the Tangential Electric Field and Surface Laplacian in EEG Classification

Cited by 3 publications

References 45 publications

The surface Laplacian technique in EEG: Theory and methods

The surface Laplacian technique in EEG: Theory and methods

An insight into the hardware and software aspects of a BCI system with focus on ultra-low power bulk driven OTA and Gm-C based filter design, and a detailed review of the recent AI/ML techniques

A robust beamforming approach for early detection of readiness potential with application to brain-computer interface systems

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