The stop signal task has been used to quantify the human inhibitory control. The inter-subject and intra-subject variability was investigated under the inhibition of human response with a realistic environmental scenario. In present study, we used a battleground scenario where a sniper-scope picture was the background, a target picture was a go signal, and a nontarget picture was a stop signal. The task instructions were to respond on the target image and inhibit the response if a nontarget image appeared. This scenario produced a threatening situation and endorsed the evaluation of how subject’s response inhibition manifests in a real situation. In this study, 32 channels of electroencephalography (EEG) signals were collected from 20 participants during successful stop (response inhibition) and failed stop (response) trials. These EEG signals were used to predict two possible outcomes: successful stop or failed stop. The inter-subject variability (between-subjects) and intra-subject variability (within-subjects) affect the performance of participants in the classification system. The EEG signals of successful stop versus failed stop trials were classified using quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA) (i.e., parametric) and K-nearest neighbor classifier (KNNC) and Parzen density-based (PARZEN) (i.e., nonparametric) under inter- and intra-subject variability. The EEG activities were found to increase during response inhibition in the frontal cortex (F3 and F4), presupplementary motor area (C3 and C4), parietal lobe (P3 and P4), and occipital (O1 and O2) lobe. Therefore, power spectral density (PSD) of EEG signals (1-50Hz) in F3, F4, C3, C4, P3, P4, O1, and O2 electrodes were measured in successful stop and failed stop trials. The PSD of the EEG signals was used as the feature input for the classifiers. Our proposed method shows an intra-subject classification accuracy of 97.61% for subject 15 with QDA classifier in C3 (left motor cortex) and an overall inter-subject classification accuracy of 71.66% ± 9.81% with the KNNC classifier in F3 (left frontal lobe). These results display how inter-subject and intra-subject variability affects the performance of the classification system. These findings can be used effectively to improve the psychopathology of attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), schizophrenia, and suicidality.

中文翻译：

---

利用受试者间和受试者内变异性预测人类抑制脑功能

停止信号任务已用于量化人类抑制控制。在现实环境场景下抑制人类反应的情况下，研究了受试者间和受试者内的变异性。在本研究中，我们使用了战场场景，其中狙击镜图片为背景，目标图片为前进信号，非目标图片为停止信号。任务指令是对目标图像做出响应，如果出现非目标图像则禁止响应。这种情景产生了一种威胁性的情况，并支持对受试者的反应抑制在真实情况下如何表现的评估。在这项研究中，从 20 名参与者在成功停止（反应抑制）和失败停止（反应）试验期间收集了 32 个通道的脑电图 (EEG) 信号。这些脑电图信号用于预测两种可能的结果：成功停止或失败停止。受试者间变异性（受试者间）和受试者内变异性（受试者内）影响分类系统中参与者的表现。使用二次判别分析（QDA）和线性判别分析（LDA）（即参数）、K-最近邻分类器（KNNC）和基于 Parzen 密度的（PARZEN）（即，非参数）在受试者间和受试者内变异下。发现额叶皮层（F3 和 F4）、前补充运动区（C3 和 C4）、顶叶（P3 和 P4）和枕叶（O1 和 O2）的脑电图活动在反应抑制期间增加。因此，在成功停止和失败停止试验中测量了 F3、F4、C3、C4、P3、P4、O1 和 O2 电极中 EEG 信号（1-50Hz）的功率谱密度（PSD）。EEG 信号的 PSD 用作分类器的特征输入。我们提出的方法显示，在 C3（左运动皮层）中使用 QDA 分类器，受试者 15 的受试者内分类准确率为 97.61%，在 F3（左额叶）中使用 KNNC 分类器，总体受试者间分类准确率为 71.66% ± 9.81% ）。这些结果显示了受试者间和受试者内的变异性如何影响分类系统的性能。这些发现可有效用于改善注意力缺陷多动障碍（ADHD）、强迫症（OCD）、精神分裂症和自杀的精神病理学。