High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis

Inertial Measurements for Tongue Motion Tracking Based on Magnetic Localization with Orientation Compensation

Permanent magnet localization (PML) is designed for applications requiring non-line-of-sight motion tracking with millimetric accuracy. Current PML-based tongue tracking is not only impractical for daily use due to many sensors being placed around the mouth, but also requires a large training set of tracer motion. Our method was designed to overcome these shortcomings by generating a local magnetic field and removing the need for the localization to be trained with tracer rotations. An inertial measurement unit (IMU) is used as a tracer that moves in a local magnetic field generated by a magnet strip. The magnetic strength can be optimized to enable the strip to be placed further away from the tracer, thus hidden from view. The tracer is small (6×6×0.8 mm³) to reduce hindrance to natural tongue movements, and the strip is designed to be worn as a neckband. The IMU's magnetometer measures the local magnetic field which is compensated for the tracer's orientation by using the IMU's accelerometer and gyroscope. The orientation-compensated magnetic measurements are then fed into a localization algorithm that estimates the tracer's 3D position. The objective of this study is to evaluate the tracking accuracy of our method. In a 8×8×5 cm³ volume, positional errors of 1.6 mm (median) and 2.4 mm (third quartile, Q3) were achieved on a tracer being rotated ±50° along both pitch and roll. These results indicate this technology is promising for tongue tracking applications.

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results.