Impact of latency jitter correction on offline P300-based classification: a preliminary study for BCI applications in...

Disorders of Consciousness (DoC) are clinical conditions characterized by different levels of arousal and awareness, including coma, Unresponsive Wakefulness Syndrome and Minimally Conscious State (MCS). A Brain-Computer Interface (BCI) employs brain signals to establish a non-muscular outward channel, representing a key frontier in the clinical care of individuals in MCS, with high potential to enhance communication and quality of life. The P300-based BCIs, which use the P300 ERP as a control signal, are the most investigated to emulate communication in MCS. However, a reliable control by MCS patients of these BCIs still remains matter of question. One major challenge could be the across trials variability of P300 characteristics, possibly related to attentional fluctuations in this population. The trial-by-trial instability of the P300 peak latency, known as latency jitter, negatively impacts classification performance, and an approach to mitigating this issue involves template matching algorithms (e.g. the Adaptive Wavelet Filtering, AWF) which detect and realign the P300 latency at the single-trial level. This study investigated the offline classification performance using Stepwise Linear Discriminant Analysis (SWLDA) models trained with progressively larger training sets, to discriminate target from non-target stimuli during an active auditory oddball paradigm. Performance from raw and jitter-corrected data, collected from a control group and a group of patients diagnosed as MCS, were compared. Results highlighted the key role of latency jitter correction in the enhancement of performance and classification speed.Clinical Relevance- The findings suggest that jitter correction could improve real-world applicability of P300-BCI systems for individuals with DoC.