An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal

Golshan, Hosein M.; Hebb, Adam O.; Hanrahan, Sara J.; Nedrud, Joshua; Mahoor, Mohammad H.

doi:10.1109/icassp.2017.7952302

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…More importantly, the classification accuracy and time complexity of existing EEG signal classification methods can still be improved. In the field of EEG recognition, two widely used joint classification methods exist: FFT-based joint method [21] and SVM-based joint method [22]. Therefore, based on the existing methods, this study proposed a novel joint method for classifying EEG signals in a complex scenario.…”

Section: Introductionmentioning

confidence: 99%

Novel joint algorithm based on EEG in complex scenarios

Chen

Yang

Miao

et al. 2019

Computer Assisted Surgery

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At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

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Section: Introductionmentioning

confidence: 99%

Novel joint algorithm based on EEG in complex scenarios

Chen

Yang

Miao

et al. 2019

Computer Assisted Surgery

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“…There are many scenarios where abnormal behavior needs to be detected, such as nursing homes, subways, prisons, construction sites, and so on. The traditional method of behavior recognition is to extract local high-dimensional visual features of video regions, then combine them into fixed size video level descriptions, and finally use a classifier for final prediction [1]. Based on deep learning methods, single stream method [2] and double stream method [3].…”

Section: Introductionmentioning

confidence: 99%

Behavior recognition based on enhanced human pose estimation

zifan,

jun

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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In public places, rapid and accurate behavior recognition helps to monitor the occurrence of dangerous events. With the rise of neural networks, many methods have been developed to handle behavior recognition problems. Unlike target recognition, behavior recognition requires not only analyzing the spatial dependencies of the target, but also analyzing the historical information of target changes. We have inherited the traditional dual stream recognition method, in order to better extract spatial and temporal features in consistency, strengthen the contextual information extraction ability and channel fusion. In this paper, we propose a parallel convolutional network (MFC network) for extracting richer contextual information from dual flow branches. In addition, we propose a spatiotemporal modal interaction module (ST block) to improve the robustness of the network. The experimental results on datasets HMDB-51 and UFC-101 show that the method is effective.

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“…Brain activities can be measured through invasive and noninvasive devices. So far, a wide research has been carried out on human behaviour and task classification using invasive techniques, such as electrocorticography (ECoG) [3] and Local Field Potentials (LFPs) [4]- [5]. In ECoG, the electrodes are placed directly on the exposed surface of the brain to record electrical activity and LFP refers to the electrical field recorded using a small-sized electrode in the extracellular space of brain tissue.…”

Section: Introductionmentioning

confidence: 99%