# Transcranial Doppler-Based Neurofeedback to Improve Hemispheric Lateralization
## 4 Discussion
In a subsequent study from the same group, TCD was employed to achieve a mean ITR of 1.08 bits/min using a real-time three-class BCI [31]. Furthermore, their approach was limited to the following specific tasks for lateralization: verbal fluency for lateralization to the left hemisphere and motor imagery of fist clenching or 3D shape tracing for the right hemisphere [31]. In these and other TCD-BCI studies, subjects have always been instructed to follow specific tasks for lateralization. Surprisingly, the idea of letting the subjects explore different strategies, possibly improving the performance of the BCIs by specifically training this capacity with a neurofeedback approach, was not previously tested. More specifically, only a few suggestions were provided about how to interact with the visual feedback, and the subjects were encouraged to freely explore different mental tasks, looking for the most effective strategies to control blood flow lateralization. Interestingly, similar neurofeedback studies based on fNIRS methodologies evidenced that free and spontaneous neurofeedback interaction may yield better results compared to following rigid instructions regarding which mental task to use [25]. Unfortunately, the present study was not designed to investigate individual variability in performance, as evidenced in Figure 4. An interesting aspect to address in future studies may be the identification of the determinants of the varying lateralization ability achieved by the different subjects, possibly including the mental strategy adopted, the motivation, the stability of cerebral hemodynamics, the fatigue accumulated during training, etc. In the present study, a free, unsupervised neurofeedback training lasting 15 min, followed by an assessment test implemented in each of the four sessions. The improvements observed across sessions demonstrate the effectiveness of the training. In addition, the increasing trend in the performance indicators, AUC, accuracy, and ITR, did not exhibit a plateau, suggesting that further benefits could be achieved by increasing the number of training sessions. Moreover, as indicated by the time course of the average responses to left and right cues (Figure 2), neurofeedback training also progressively shortens the time required to significantly separate the two sets, which suggests that training would also increase BCI performance by allowing for a progressively shorter duration of the tasks. In agreement with [PERSON] et al. [47], we observed that a three-class performs slightly better than a two-class classification. In our case, this confirmed the hypothesis that discarding the weakest, i.e., the most \"doubftul\" responses, improves the ITR, even though discarding certain trials reduces the number of valid trials in the overall time dedicated to the experiment. In fact, as graphically represented in Figure 5, the highest ITR is obtained as the best trade-off between increasing the accuracy of the selection and limiting the number of discarded trials. It can be observed that the ITR obtained in the present study is extremely low. However, the assessment of the ITR was only meant to quantify the improvement across sessions or between two- and three-class decoding. In fact, a very simple signal processing was adopted here, and large time intervals were dedicated to tasks and rest. More elaborate processing and classification techniques have been proposed and tested in several studies [33, 48], and shorter time intervals [47] can be adopted to achieve higher ITR, which is adequate for BCI applications. In addition, to enhance the performance of TCD-based BCIs, integrating additional modalities has proven effective. For example, combining TCD with EEG to capture neuronal activity improved classification accuracy but increased computational complexity [49]. Similarly, [PERSON] et al. combined TCD with fNIRS to enhance spatial resolution and accuracy [50]. Artificial intelligence methods have also been explored to optimize classification algorithms and improve system robustness [51].