Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex

Abstract

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Fig. 1.

(a) One coronal slice of the head mesh containing four layers: brain, CSF, skull, scalp. Neural activities were modeled in superior temporal sulcus inside the yellow circle. (b) Placements of electrodes and DOT probes. Black dots represent the locations of EEG electrodes, red and blue dots represent the locations of DOT sources and detectors. Yellow region shows the area where neural activities were modeled. (c) Histogram of DOT channel lengths up to 70 mm.

Fig. 2.

Flowchart of the data-informed selective channel method on DOT reconstruction.

Fig. 3.

(a) Distribution of simulated DOT signals of four neural sources at different depths under the noise-free condition. X axis denotes DOT channel lengths and y axis denotes the absolute value of optical densities. (b) Distribution of simulated DOT signals of four neural sources at different depths under low-level noise. (c) Distribution of simulated DOT signals of four neural sources at different depths under high-level noise.

Fig. 4.

(a)Reconstruction depths of 9 sources using 14 different DOT channel length thresholds under noise-free condition. Black stars represent selected sources and corresponding optimal DOT channel lengths for curve fitting. (b) Reconstruction depths under low-level noise condition. (c) Reconstruction depths under high-level noise condition. (d) Fitted exponential decay curve using 4 selected DOT channel length thresholds and corresponding standard deviations under noise-free condition. (e) Fitted exponential decay curve under low-level noise condition. (f) Fitted exponential decay curve under high-level noise condition.

Fig. 5.

(a) DOT reconstructions at four different depths: 13 mm, 17 mm, 20 mm, and 23 mm using fixed DOT channels ≤ 70 mm under noise-free conditions. Green dots represent the center of ground truths. (b) DOT reconstructions at four different depths: 13 mm, 17 mm, 20 mm, and 23 mm using selective DOT channel lengths under noise-free conditions.

Fig. 6.

(a) DOT reconstructions at four different depths: 13 mm, 17 mm, 20 mm, and 23 mm using fixed DOT channels ≤ 70 mm under low-level noise condition. Green dots represent the center of ground truths. (b) DOT reconstructions at four different depths: 13 mm, 17 mm, 20 mm, and 23 mm using selective DOT channel lengths under low-level noise condition.

Fig. 7.

(a) DOT reconstructions at four different depths: 13 mm, 17 mm, 20 mm, and 23 mm using fixed DOT channels ≤ 70 mm under high-level noise condition. (b) DOT reconstructions at four different depths: 13 mm, 17 mm, 20 mm, and 23 mm using selective DOT channel lengths under high-level noise condition.

Fig. 8.

(a) The side views and slice views of neural source, EEG-only reconstruction, EEG-DOT using selective channels under noise-free conditions, EEG-DOT using selective channels under low-level DOT noise and high-level DOT noise conditions for a shallow neural source (13 mm). (b) The side views and slice views of neural source, EEG-only reconstruction, EEG-DOT using selective channels under noise-free conditions, EEG-DOT using selective channels under low-level DOT noise and high-level DOT noise conditions for a shallow neural source (20 mm).

Fig. 9.

Neural source depth versus selected DOT channel threshold using the data-informed selective method.

Fig. 10.

Figure 10. DOT reconstruction depth using channels shorter than 70 mm and DOT reconstruction using selective channels for 9 neural source depths under noise-free condition (a), for low-level noise (b), and high-level noise (c). EEG reconstruction depth without priors, with DOT priors using channels shorter than 70 mm and DOT priors using selective channels for 9 neural source depths under noise-free condition (d), for low-level noise (e), and high-level noise (f).

Fig. 11.

(a) Full width half maximum (FWHM) of EEG reconstruction without priors, with DOT priors using channels shorter than 70 mm and DOT priors using selective channels for 9 neural source depths under noise-free condition. (b) FWHM of EEG reconstruction without priors, with DOT priors using channels shorter than 70 mm and DOT priors using selective channels for 9 neural source depths under low-level noise condition. (c) FWHM of EEG reconstruction without priors, with DOT priors using channels shorter than 70 mm and DOT priors using selective channels for 9 neural source depths under high-level noise condition.

Fig. 12.

Block diagram of clinical applications of the data-informed selective channel DOT reconstruction.