Classification of slow cortical potential (SCP) signals is crucial for brain-computer interface systems. This paper presents a new scheme to improve the classification performance of SCP signals. It consists of two parts: 1) by fitting the wavelet coefficients of SCP signals with second-order polynomial, the SCP trends are extracted; 2) a voting system based on the optimal training parameters of the support vector machines is developed to enhance the classification accuracy. Experimental results reveal that the proposed scheme outperforms state-of-theart methods. The classification accuracy improvements for both the dataset Ia of the BCI competition II and the TJU dataset (the dataset was collected in Tianjin University, termed TJU dataset) are reported.

Smoke is an informative indicator of early fire and gas leakage. Segmenting the smoke from images can provide detailed information about the smoke volume, dispersion direction, and source location, which has significant implications considering the proliferation of video surveillance systems in cities. Focusing on smoke segmentation in the urban scene, we designed a dual-branch segmentation model, named SmokeSeger, which couples a transformer branch and a convolutional neural network (CNN) branch to enhance the representation of both global and local features. To address the lack of real-scene smoke datasets, we built an urban scene smoke segmentation dataset containing 3217 images of fire smoke and exhaust emissions with accurate annotations. Experiments validate that the SmokeSeger outperforms other mainstream segmentation methods on the proposed dataset. Visualization of attention maps reveals that the model could effectively capture the semantic relationship between the smoke and the corresponding source, which benefits the discrimination between smoke and smoke-like objects. More details available at https://github.com/VisAcademic/SmokeSeger .

In-vehicle air pollution, especially the volatile organic compounds (VOCs) pollutants, is an important source of passenger discomforts and consumer complaints. In this work, we designed a portable instrument based on an electronic nose (e-nose) to address the task of subjective assessment of in-vehicle air quality (IAQ). Our instrument consists of three modules: 1) the preconcentrating module that concentrates the VOCs to detectable level through adsorption and desorption; 2) the sensing module that measures the odor fingerprints of the target gas with an array of metal oxide semiconductor sensors; 3) the processing module that classifies the measured responses into the corresponding subjective grades via a stacking-based ensemble learning method. A dataset for subjective IAQ assessment was built to train and test the e-nose, which contains gas sample responses measured by the instrument and labels obtained by jury tests. Grading experiment results validated the practical performance of the proposed instrument as a direct case of narrowing gaps between objective assessments and subjective perceptions based on instrumentation.

Objective. Depression is a common chronic mental disorder characterized by high rates of prevalence, recurrence, suicide, and disability as well as heavy disease burden. An accurate diagnosis of depression is a prerequisite for treatment. However, existing questionnaire-based diagnostic methods are limited by the innate subjectivity of medical practitioners and subjects. In the search for a more objective diagnostic methods for depression, researchers have recently started to use deep learning approaches. Approach. In this work, a deep-learning network, named adaptively multi-time-window graph convolutional network (GCN) with long-short-term memory (LSTM) (i.e. AMGCN-L), is proposed. This network can automatically categorize depressed and non-depressed people by testing for the existence of inherent brain functional connectivity and spatiotemporal features contained in electroencephalogram (EEG) signals. AMGCN-L is mainly composed of two sub-networks: the first sub-network is an adaptive multi-time-window graph generation block with which adjacency matrices that contain brain functional connectivity on different time periods are adaptively designed. The second sub-network consists of GCN and LSTM, which are used to fully extract the innate spatial and temporal features of EEG signals, respectively. Main results. Two public datasets, namely the patient repository for EEG data and computational tools, and the multi-modal open dataset for mental-disorder analysis, were used to test the performance of the proposed network; the depression recognition accuracies achieved in both datasets (using tenfold cross-validation) were 90.38% and 90.57%, respectively. Significance. This work demonstrates that GCN and LSTM have eminent effects on spatial and temporal feature extraction, respectively, suggesting that the exploration of brain connectivity and the exploitation of spatiotemporal features benefit the detection of depression. Moreover, the proposed method provides effective support and supplement for the detection of clinical depression and later treatment procedures.

Touch is one of the most essential and effective means to convey affective feelings and intentions in human communication. For a social robot, the ability to recognize human touch gestures and emotions could help realize efficient and natural human–robot interaction. To this end, an affective touch gesture dataset involving ten kinds of touch gestures and 12 kinds of discrete emotions was built by using a pressure sensor array, in which the acquired touch gesture samples are three-dimensional (3-D) spatiotemporal signals that include the shape appearance and motion dynamics. Due to the excellent performance of convolutional neural networks (CNNs), spatiotemporal CNNs have been effectively verified by researchers for 3-D signal classification. However, the large number of parameters and the high complexity of training 3-D convolution kernels remain to be solved. In this article, a decomposed spatiotemporal convolution was designed for feature representation from the raw touch gesture samples. Specifically, the 3-D kernel was factorized into three 1-D kernels by tensor decomposition. The proposed convolution has a simpler but deeper architecture than standard 3-D convolution, which improves the nonlinear expression ability of the model. Besides, the computation cost can be reduced without compromising recognition accuracy. Using a user-dependent test mode, the proposed method yields the accuracies of up to 92.41% and 72.47% for touch gesture and emotion recognitions, respectively. Experimental results demonstrate the effectiveness of the proposed method, and at the same time, preliminarily verify the feasibility of robot perceiving human emotions through touch.