The interactions between paralyzed individuals with severe physical disabilities and smart infrastructure need to be facilitated, and the tongue⁻computer interface (TCI) provides an efficient and feasible solution. By attaching a permanent magnet (PM) on the apex of the tongue, the real-time tongue motion tracking can be switching to solve a nonlinear inverse magnetic problem. This paper presents a proof-of-concept prototype TCI system utilizing a combined T-type PM marker for potential environment control. The introduction of the combined T-type PM promotes the anisotropy of the magnetic field distribution. A comprehensive calibration method for the sensing system is proposed to figure out the bias in the magnetic moment of the PM marker and the sensing axis rotation of the sensors. To address the influence of initialization in solving the overdetermined inverse magnetic problem, an adaptive Levenberg⁻Marquardt algorithm is designed utilizing real-time measurements. Bench-top experiments were carried out based on a high-precision three-dimensional (3D) translation platform, and the feasibility of the proposed TCI system in magnetic localization accuracy and efficiency is fully assessed. The mean localization error is 1.65 mm with a mean processing time of 65.7 ms, and a mean improvement of 54.7% can be achieved compared with a traditional LM algorithm.