Investigation of quantitative EEG-based indices and their role in post-stroke neuromotor rehabilitation protocols supported by Brain-Computer Interfaces

Stroke is a widespread neurological disorder that affects millions of individuals worldwide each year, resulting in a high incidence of functional disability and mortality. The impact of stroke is far-reaching, frequently resulting in long-term disabilities, predominantly affecting the upper limb, and influencing multiple aspects of everyday life of stroke survivors. It is a heterogeneous pathology, influenced by several factors including the specific brain regions involved. This heterogeneity is also evident in the range of rehabilitation treatments that are currently available and under investigation. Among the innovative approaches in stroke rehabilitation, Brain-Computer Interface (BCI) technologies, administered as an add-on to standard therapy, have emerged as promising tools, demonstrating significant efficacy in facilitating motor recovery. Furthermore, protocols based on motor imagery (MI) supported by BCI systems have demonstrated encouraging outcomes, offering a pivotal alternative access point to brain regions implicated in motor control. Nevertheless, further investigation is essential to optimise these interventions, with the objective of developing a more personalised rehabilitation pathway tailored to the specific needs of each individual patient. The investigation of biomarkers, predictors, and determinants that influence treatment responses is of paramount importance in this context as they reflect the active interaction between the patient's unique characteristics and the rehabilitation treatment provided. The identification and characterisation of biomarkers facilitate the quantification of the neurophysiological condition of patients beyond the conventional clinical scales. Additionally, this enables a more in-depth comprehension of the motor recovery process. This thesis examines the relationship between the individual characteristics of patients, expressed in form of electroencephalographic (EEG) based indices, and the rehabilitation process, with the overarching objective of improving treatment efficacy. The EEG-based indices identified through an extensive literature review are fully explored and reported in this PhD thesis. Specifically, the investigation focuses on Low Frequency Oscillations (LFOs), relative power indices, power ratio indices, and brain symmetry indices within the context of post-stroke upper limb rehabilitation supported by MI-BCI. The aforementioned indices were analysed on EEG traces collected from both healthy participants and post-stroke patients who were engaged in experimental tasks that are typically employed in the context of MI-BCI rehabilitation protocols . The pipeline and methods for the extraction of EEG-based indices were refined and evaluated. The impact of MI-BCI training on the recovery of upper limb function in post-stroke patients was also assessed using a neurophysiological and clinical approaches, encompassing both short- and long-term effects. Moreover, the pathological conditions of the patients, specifically their level of impairment at baseline, quantified by the clinical scales, were also taken into account in order to identify the optimal patient profiles that may achieve better outcomes in terms of motor recovery following a MI-BCI training. The main findings indicate that the LFOs power can be employed in rehabilitative protocols centered on motor imagery tasks. Furthermore, the relative power, the power ratio and the brain symmetry indices effectively reflect the motor impairment level of patients and, additionally, the brain symmetry indices exhibit a potential in predicting functional motor recovery following MI-BCI training. Notably, the efficacy of MI-BCI training is influenced by the baseline level of impairment, suggesting that patients with a more severe level of motor impairment may experience better outcomes. Collectively, the findings presented in this thesis contribute to the existing body of knowledge regarding the interaction between quantitative EEG-based indices and rehabilitation treatments, underscoring their potential as biomarkers, predictors, or determinants of response to MI-BCI interventions. By aligning rehabilitation efforts with individual patient clinical and neurophysiological profiles, the recovery process could be significantly enhanced, paving the way for more effective, patient-centered care in the future.