Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

Abstract

The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.

Decreased Npas4 expression in patients with post-stroke depression

“…The P300 amplitude indicates the subjects' ability to receive outside information, and the P300 latency objectively reflects cognitive function, judgment, and other advanced thinking activities [17]. The majority of studies on P300 in cognitive impairment reported prolonged latencies and reduced amplitudes in visual or auditory modalities [18]. In this study, we found that patients after stroke have deficits in perception and/or cognitive processing.…”

The Current Research of Spatial Cognitive Evaluation and Training With Brain–Computer Interface and Virtual Reality

“…The power of EEG signals from AD patients on alpha and theta frequency bands was significantly higher relative to normal control when performing object-location memory tasks (Han et al, 2017). In addition, when the delay, peak amplitude, and root mean square of P300 act as the features in visual spatial memory paradigm, the classification accuracy could achieve up to 91.76% (Li et al, 2013).…”

P300 characterization using empirical mode decomposition

“…The approach has also been applied to identify neural activity sources related to ERPs such as described in Li et al [20] which used EMD to detect disease states following a stroke. EMD has the potential to be used to identify taskrelevant activity from trial to trial in different individuals, because of the adaptive data decomposition it provides.…”