0000000000189527
AUTHOR
Xiaoshuang Wang
The Approach Behavior to Angry Words in Athletes—A Pilot Study
An increasing number of studies have found that athletes have a higher level of aggression than non-athletes. Anger is an important factor in the generation of aggressive behavior, and anger has been found to relate to both approach behavior and avoidance behavior. The present pilot study compared the aggression level of athletes and non-athletes using the Buss-Perry Aggression Questionnaire, and examined the responses of participants to anger-related stimuli using the manikin task, a paradigm that measures approach-avoidance behavior. In total, 15 athletes and 15 non-athletes finished the questionnaire and the manikin task, which included two conditions. In the anger approach condition, pa…
One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-sec sliding windows. Then, the 2-sec interictal and ictal segments w…
One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG
Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all c…
Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG
The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the combination with conventional machine learning methods. Thus, channel selection combined with deep learning methods can be further analyzed in the field of seizure prediction. Given this, in this work, a novel iEEG-based deep learning method of One-Dimensional Convolutional Neural Networks (1D-CNN) combined with channel increment strategy was proposed for the effective seizure prediction. First, we used 4-sec sliding windows without overlap to segment iEEG signals. Then, 4-sec …
One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals
Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…
Seizure Prediction Using EEG Channel Selection Method
Seizure prediction using intracranial electroencephalogram (iEEG) is still challenging because of complicated signals in spatial and time domains. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, in this paper, a novel approach of iEEG channel selection strategy combined with one-dimensional convolutional neural networks (1D-CNN) was presented for seizure prediction. First, 15-sec and 30-sec iEEG segments with an increasing number of channels (from one channel to all channels) were sequentially fed into 1D-CNN models for training and testing. Then, the channel case with the best classification rate was selected for each partici…
The Approach Behavior to Angry Words in Athletes : A Pilot Study
An increasing number of studies have found that athletes have a higher level of aggression than non-athletes. Anger is an important factor in the generation of aggressive behavior, and anger has been found to relate to both approach behavior and avoidance behavior. The present pilot study compared the aggression level of athletes and non-athletes using the Buss-Perry Aggression Questionnaire, and examined the responses of participants to anger-related stimuli using the manikin task, a paradigm that measures approach-avoidance behavior. In total, 15 athletes and 15 non-athletes finished the questionnaire and the manikin task, which included two conditions. In the anger approach condition, pa…