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Determining the influence of visual training on EEG activity patterns using association rule mining

- Yan, Fangang, Watters, Paul, Wang, Wei

  • Authors: Yan, Fangang , Watters, Paul , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: To confirm that visual training can change EEG patterns by association rule mining method, firstly, we collected the EEG of people who are under a long-term visual professional training (visual training group) and novice people (control group) during a specific mental tasks. Secondly, we determined the difference of brain electrical activity between the two groups using machine learning methods. Thirdly, we discovered distinct patterns using association rule algorithm, finding that the two groups were separable based on their completion of visual professional cognitive tasks. In the beta band, visual training group showed a specific and significant association pattern which included FP1 and C4. The results indicate that the EEG patterns were modified because of visual professional training. We further discuss the impact of long-term visual professional training on the EEG. © 2011 IEEE.

K-complex detection using a hybrid-synergic machine learning method

- Vu, Huy Quan, Li, Gang, Sukhorukova, Nadezda, Beliakov, Gleb, Liu, Shaowu, Philippe, Carole, Amiel, Hélène, Ugon, Adrien

  • Authors: Vu, Huy Quan , Li, Gang , Sukhorukova, Nadezda , Beliakov, Gleb , Liu, Shaowu , Philippe, Carole , Amiel, Hélène , Ugon, Adrien
  • Date: 2012
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Systems, Man and Cybernetics Part C : Applications and Reviews Vol. 42, no. 6 (2012), p. 1478-1490
  • Full Text: false
  • Reviewed:
  • Description: Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection. © 1998-2012 IEEE.
  • Description: 2003010569

A weighted overlook graph representation of eeg data for absence epilepsy detection

- Wang, Jialin, Liang, Shen, Wang, Ye, Zhang, Yanchun, Ma, Jiangang

  • Authors: Wang, Jialin , Liang, Shen , Wang, Ye , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 20th IEEE International Conference on Data Mining, ICDM 2020 Vol. 2020-November, p. 581-590
  • Full Text: false
  • Reviewed:
  • Description: Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances. © 2020 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate "Jiangang Ma“ is provided in this record**
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