Optimizing Spatial Filters to reduce BCI Inefficiency

ABrain-Computer Interface (BCI) establishes a communication between a person and an electronic device, allowing the user to control the device by means of just his/her brain signals. While BCI techniques can be successfully used for game and entertainment applications, the most important BCI aim is to improve the everyday life of people with severe neurodisabilities. Therefore, a high reliability of the BCI system is required to obtain a larger acceptance by the medical community. Indeed, the BCI systems do not work properly for a non negligible part of the population, which is estimated to be around 25%. This phenomenon is named BCI inefficiency, referring to the inability of BCI systems to successfully deal with all typology of brain signals. The BCI inefficiency phenomenon affects all kinds of BCIs. Nevertheless, little effort has been invested so far to understand the causes of BCI inefficiency and to reduce it. This work is the first attempt to build an overview on BCI inefficiency for electroencephalogram (EEG) BCIs based on sensorimotor rhythm (SMR) to better understand and consequently face it. The problem is analyzed from several points of view, each of them offering a new insight and all of them converging in the design of a new spatial filter algorithm whose efficiency is demonstrated in a final online study. In the first part of this thesis, a large scale screening study is presented, with a categorization of BCI users in three groups and grand average EEG data investigations directed towards understanding the differences in the SMR activity among these groups. These preliminary results allow to conduct a goal-oriented investigation of the EEG data by means of novel machine learning and statistical methods, which takes up the second part of the thesis. In particular, several analyses are carried out, focusing on the data dimensionality, complexity and information content, as well as on the practical issue of the number and position of the EEG channels. These problems, together with the experiment’s practicability and the offered spatial resolution, constitute the trade-off dilemma between the quantity of the information and the sensibility to overfitting of Common Spatial Patterns (CSP), the most common spatial filter algorithm for SMR-based BCIs. Additionally, the role of pre-stimulus SMR on the user’s performance is investigated. In the third part of the thesis, the previous analyses are taken into account, giving rise to the development of a new spatial filter algorithm called Common Spatial Pattern Patches (CSPP), a compromise between Laplacian filters and CSP. Being more robust than CSP against overfitting, as demonstrated in offline comparison to CSP and its recent regularized version R-CSP, CSPP can be employed very early in the experiment and is very suitable for the co-adaptive calibration, a new experimental approach designed to alleviate BCI inefficiency. An online study with 20 volunteers who participated earlier in at most two experiments and had difficulties to reach BCI control, proved that CSPP, given its properties of being local and low dimensional, can be successfully employed to construct subject-independent classifiers and to be combined with supervised and unsupervised adaptation techniques, therefore optimizing the co-adaptive design. Indeed, all users except for three could improve their previous BCI performance resulting in a strong reduction of BCI inefficiency.