The main shortcomings of using electrocortical stimulation (ECS) in identifying the motor functional area around the focus in neurosurgery are certainly time-consuming, possibly cerebral cortex injuring and perhaps triggering epilepsy. To solve these problems, we in our research presented an intraoperative motor cortex functional mapping based on electrocorticography (ECoG). At first, using power spectrum estimation, we analyzed the characteristic of ECoG which was related to move task, and selected Mu rhythm as the move-related feature. Then we extracted the feature from original ECoG by multi-resolution wavelet analysis. By calculating the sum value of feature in every channel and observing the distribution of these sum values, we obtained the correlation between the cortex area under the electrode and motor cortex functional area. The results showed that the distribution of the relationship between the cortex under the electrode and motor cortex functional area was almost consistent with those identified by ECS which was called as ‘the gold-standard’. It indicated that this method was basically feasible, and it just needed five minutes totally. In conclusion, ECoG-based and passive identification of motor cortical function may serve as a useful adjunct to ECS in the intraoperative mapping.
In order to accurately localize the image coordinates and serial numbers of intraoperative subdural matrix electrodes, a matrix electrode localization algorithm for image processing is proposed in this paper. Firstly, by using point-by-point extended electrode location algorithm, the electrode is expanded point-by-point vertically and horizontally, and the initial coordinates and serial numbers of each electrode are determined. Secondly, the single electrode coordinate region extraction algorithm is used to determine the best coordinates of each electrode, so that the image coordinates and serial numbers of all electrodes are determined point-by-point. The results show that the positioning accuracy of electrode serial number is 100%, and the electrode coordinate positioning error is less than 2 mm. The algorithms in this paper can accurately localize the image coordinates and the serial numbers of a matrix electrode arranged in an arc, which could aid drawing of cortical function mapping, and achieve precise positioning of brain functional areas, so that it can be widely used in neuroscience research and clinical application based on electrocorticogram analysis.