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This paper deals with the co-registration of an MRI scan with EEG sensors. We set out to evaluate the effectiveness of a 3D handheld laser scanner, a device that is not widely used for co-registration, applying a semi-automatic procedure that also labels EEG sensors. The scanner acquired the sensors' positions and the face shape, and the scalp mesh was obtained from the MRI scan. A pre-alignment step, using the position of three fiducial landmarks, provided an initial value for co-registration, and the sensors were automatically labeled. Co-registration was then performed using an iterative closest point algorithm applied to the face shape. The procedure was conducted on five subjects with two scans of EEG sensors and one MRI scan each. The mean time for the digitization of the 64 sensors and three landmarks was 53 s. The average scanning time for the face shape was 2 min 6 s for an average number of 5,263 points. The mean residual error of the sensors co-registration was 2.11 mm. These results suggest that the laser scanner associated with an efficient co-registration and sensor labeling algorithm is sufficiently accurate, fast and user-friendly for longitudinal and retrospective brain sources imaging studies.

Several studies have described cranio-cerebral correlations in accordance with the 10-20 electrode placement system. These studies have made a significant contribution to human brain imaging techniques, such as near-infrared spectroscopy and trans-magnetic stimulation. With the recent development of high resolution EEG, an extension of the 10-20 system has been proposed. This new configuration, namely the 10-10 system, allows the placement of a high number (64-256) of EEG electrodes. Here, we describe the cranio-cerebral correlations with the 10-10 system. Thanks to the development of a new EEG-MRI sensor and an automated algorithm which enables the projection of electrode positions onto the cortical surface, we studied the cortical projections in 16 healthy subjects using the Talairach stereotactic system and estimated the variability of cortical projections in a statistical way. We found that the cortical projections of the 10-10 system could be estimated with a grand standard deviation of 4.6 mm in x, 7.1 mm in y and 7.8 mm in z. We demonstrated that the variability of projections is greatest in the central region and parietal lobe and least in the frontal and temporal lobes. Knowledge of cranio-cerebral correlations with the 10-10 system should enable to increase the precision of surface brain imaging and should help electrophysiological analyses, such as localization of superficial focal cortical generators.

Spatial localization of scalp EEG electrodes is a major step for dipole source localization and must be accurate, reproducible and practical. Several methods have been proposed in the last 15 years. The most widely used method is currently electromagnetic digitization. Nevertheless, this method is difficult to use in a clinical environment and has not been validated with a high number of electrodes. In this paper, we introduce a new automatic method for localizing and labeling EEG sensors using MRI. First, we design a new scalp EEG sensor. Secondly, we validate this new technique on a head phantom and then in a clinical environment with volunteers and patients. For this, we compare the reproducibility, accuracy and performance of our method with electromagnetic digitization. We demonstrate that our method provides better reproducibility with a significant difference (p<0.01). Concerning precision, both methods are equally accurate with no statistical differences. To conclude, our method offers the possibility of using MRI volume for both source localization and spatial localization of EEG sensors. Automation makes this method very reproducible and easy to handle in a routine clinical environment.

Aim of the Study: An important goal for EEG-based functional brain studies is to estimate the location of brain sources that produce the scalp-recorded signals. Such source localization requires locating precisely the position of the EEG sensors. This review describes and compares different methods that are used for localizing EEG sensors., Results: Five different methods have been described in literature. Manual methods consist in manual measurements to calculate the 3D coordinates of the sensors. Electromagnetic and ultrasound digitization permit localization by using trade devices. The photogrammetry system consists in taking pictures of the patient's head with the sensors. The last method consists in directly localizing the EEG sensors in the MRI volume., Discussion and Conclusions: The spatial localization of EEG sensors is an important step in performing source localization. This method should be accurate, fast, reproducible, and cheap. Currently, electromagnetic digitization is the most currently used method but MRI localization could be an interesting way because no additional method or device needs to be used to locate the EEG sensors.