# Transcranial Doppler-Based Neurofeedback to Improve Hemispheric Lateralization
## 4 Discussion  
In this work, we proposed a neurofeedback system based on hemispheric lateralization, utilizing transcranial Doppler technology, implemented with cost-effective and portable components. While the system can operate on different cerebral arteries, this study was conducted on the middle cerebral arteries, as they supply the cognitive and motor regions of the brain. The results indicate that neurofeedback training over four sessions effectively improves the individual capacity to voluntarily orient blood flow to the right or left side of the brain, as documented by increasing separation of average LI responses to left/right mental efforts, as well as by increased accuracy and AUC of ROC curves related to the binary classification of the responses. As a side investigation, we verified that excluding \"doubful\" responses could further increase the performance of the decoding, according to a three-class classification. Finally, a questionnaire administered at the end of the first session revealed that the subjects successfully experimented with new, original mental strategies to achieve the goal of driving the ball. To the best of our knowledge, only two studies have investigated the use of TCD to implement neurofeedback systems. In both studies, the focus was on either increasing or decreasing blood flow in MCAs [33] or anterior cerebral arteries (ACAs) [34]. [PERSON] et al. [33] divided participants into two separate groups tasked with increasing or decreasing CBFV. Without a prior training phase, they asked participants to modify flow velocity during eight sessions, each consisting of six extended neurofeedback trials lasting four minutes. Significant success was observed from the second session on, for increasing flow, and from the seventh session on, for decreasing it [33]. Similarly, [PERSON] et al. [34] presented a neurofeedback system tested on two groups using different protocols, one longer (2 min tasks, repeated six times across four sessions) and one shorter (30 s tasks, repeated five times across three sessions). They reported a progressively increasing success rate with training and better performance with shorter-lasting tasks. However, the paper mainly presented, as a proof of concept, a description of the necessary hardware to achieve the real-time acquisition and processing of blood velocity signals and presentation of a visual feedback to the subject, with no statistical analysis to support the experimental findings, due to the low sample size [34]. Notably, in both studies, a single blood velocity signal related to one given artery, or to the average of left and right arteries, was provided as visual feedback; therefore, the possible difference between the two signals was not assessed. In this respect, the present study introduces a novel approach, i.e., by adopting as visual feedback the difference in CBFVs collected from the two sides, the user is trained to unbalance the blood flow to the left or right hemisphere. The idea of measuring the lateralization of brain function by assessing hemispheric flow imbalance dates back to the 1970s [43] and was later been implemented with different methodologies. Besides fMRI and more recently, fNIRS, lateralization has been frequently investigated by TCD, e.g., for assessing the lateralization of speech and language functions [30; 44]. In addition, the TCD-detected lateralization of brain functions has been employed for implementing BCI applications [45; 46; 47]. A first attempt was made by [PERSON] et al. [47], who investigated the possibility to discriminate a word task (mental generation of a sequence of words, all starting with the same given letter), expected to lateralize blood flow to the left side, from a mental rotation task (determination of whether two presented, differently-oriented objects are the same object or mirror images), expected to induce a bilateral flow increase. A similar approach (lateralizing vs. non-lateralizing task) was later used to operate a keyboard [46].