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gcb-net  (SoftMax Inc)

 
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    Structured Review

    SoftMax Inc gcb-net
    Gcb Net, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gcb-net/gcb+net/pm37708717-246-366-368
    Average 90 stars, based on 1 article reviews
    gcb-net - by Bioz Stars, 2026-09
    90/100 stars

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    Sampling:

    Article Title: Emotion recognition in EEG signals using deep learning methods: A review.
    Article Snippet: Works Year Dataset Number of Cases Preprocessing DL Model Classifier Toolbox Performance Ceria (%) [29] 2018 SEED 15 Subjects DE HCNN NA MATLAB Acc (Beta) = 86.2 Acc(Gamma) = 88.2 [30] 2021 SEED, DEAP 15 (SEED), 32 (DEAP) CWT 2D-CNN Softmax MATLAB Acc = 49.12 (3-class) Acc = 54 (2-class) [31] 2023 DEAP 32 Subjects time, frequency and timefrequency features 2D-CNN NA PyTorch Acc-val = 98.36 Acc-aro = 98.27 Acc = 98.38(4-class) [32] 2022 DEAP 32 Subjects 3D spatial-spectral features DBCN Softmax NA Acc-val = 90.93 (subject-dependent) Acc-aro = 89.67 (subject-dependent) Acc-val = 83.98 (subject-independent) Acc-aro = 79.45 (subject-independent) [33] 2018 DEAP 32 Subjects Down sampling, STFT 2D-CNN Softmax TensorFlow Acc-aro = 76.56 Acc-val = 80.46 [34] 2019 DEAP 32 Subjects MFM CapsNet NA TensorFlow Acc-aro = 68.28 Acc-val = 66.73 Acc-dom = 67.25 [35] 2019 DEAP, SEED 15 (SEED), 32 (DEAP) Spectrogram, BoDF AlexNet SVM NA Acc = 93.8 [36] 2019 DREAMER, AMIGOS, MAHNOB-HCI, DEAP Different Subjects Frequency filters, DE, PSE, RGB heat-map, PCA VGG16 for feature extraction, LSTM ELM NA Acc = 81.05 [37] 2020 DEAP 32 Subjects 3D multiscale sample entropy matrix 2D-CNN HMM TensorFlow Acc-val = 79.77 Acc-aro = 83.09 Acc-dom = 81.83 [38] 2021 DEAP, SEED, DREAMER, AMIGOS 32 Subjects Topographic and holographic feature maps 2D-CNN SVM MATLAB Acc = 89.31 [39] 2021 DEAP 32 Subjects STFT, DA algorithm Borderline-SMOTE 1D-CNN Softmax NA Acc-val = 97.47 Acc-aro = 97.76 [40] 2021 SEED 15 Subjects CWT, DE 2D-CNN Softmax Python Acc = 91.45 [41] 2019 DEAP 32 Subjects Normalization, PSD 2D-CNN Softmax NA Acc = 88.76 [42] 2020 Clinical Different Subjects ICA 1D-CNN Softmax NA Acc = 92.44 [43] 2019 DEAP 32 Subjects k-means ECNNs Plurality voting NA Acc = 82.92 [44] 2020 DEAP 32 Subjects 3D EEG stream representation based on spatio-temporal information 3D-CNN NA PyTorch Acc-val = 99.11(2- class) Acc-aro = 99.74(2- class) Acc = 99.73(4-class) [45] 2018 SEED, DREAMER 15 (SEED), 23 (DREAMER) Adjacency matrix, Different features DGCNN Softmax NA Acc = 79.95(subject independent) Acc = 90.4(subject dependent) [46] 2019 SEED, DEAP 15 (SEED), 32 (DEAP) DE, PLV connectivity GCCN Softmax NA Acc = 84.35 [47] 2019 SEED, DREAMER 15 (SEED), 23 (DREAMER) DE, PSD, DASM, RASM, DCAU, LDS, BLS GCB-net Softmax NA Acc = 94.24 [48] 2019 DEAP 32 Subjects Normalization Multi-column CNN Voting Pytorch Acc-val = 90.01 Acc-aro = 90.65 [49] 2020 DEAP 32 Subjects Brain connectivity, PCC, PLV, TE 2D-CNN Softmax PyTorch.

    Extraction:

    Article Title: Emotion recognition in EEG signals using deep learning methods: A review.
    Article Snippet: Works Year Dataset Number of Cases Preprocessing DL Model Classifier Toolbox Performance Ceria (%) [29] 2018 SEED 15 Subjects DE HCNN NA MATLAB Acc (Beta) = 86.2 Acc(Gamma) = 88.2 [30] 2021 SEED, DEAP 15 (SEED), 32 (DEAP) CWT 2D-CNN Softmax MATLAB Acc = 49.12 (3-class) Acc = 54 (2-class) [31] 2023 DEAP 32 Subjects time, frequency and timefrequency features 2D-CNN NA PyTorch Acc-val = 98.36 Acc-aro = 98.27 Acc = 98.38(4-class) [32] 2022 DEAP 32 Subjects 3D spatial-spectral features DBCN Softmax NA Acc-val = 90.93 (subject-dependent) Acc-aro = 89.67 (subject-dependent) Acc-val = 83.98 (subject-independent) Acc-aro = 79.45 (subject-independent) [33] 2018 DEAP 32 Subjects Down sampling, STFT 2D-CNN Softmax TensorFlow Acc-aro = 76.56 Acc-val = 80.46 [34] 2019 DEAP 32 Subjects MFM CapsNet NA TensorFlow Acc-aro = 68.28 Acc-val = 66.73 Acc-dom = 67.25 [35] 2019 DEAP, SEED 15 (SEED), 32 (DEAP) Spectrogram, BoDF AlexNet SVM NA Acc = 93.8 [36] 2019 DREAMER, AMIGOS, MAHNOB-HCI, DEAP Different Subjects Frequency filters, DE, PSE, RGB heat-map, PCA VGG16 for feature extraction, LSTM ELM NA Acc = 81.05 [37] 2020 DEAP 32 Subjects 3D multiscale sample entropy matrix 2D-CNN HMM TensorFlow Acc-val = 79.77 Acc-aro = 83.09 Acc-dom = 81.83 [38] 2021 DEAP, SEED, DREAMER, AMIGOS 32 Subjects Topographic and holographic feature maps 2D-CNN SVM MATLAB Acc = 89.31 [39] 2021 DEAP 32 Subjects STFT, DA algorithm Borderline-SMOTE 1D-CNN Softmax NA Acc-val = 97.47 Acc-aro = 97.76 [40] 2021 SEED 15 Subjects CWT, DE 2D-CNN Softmax Python Acc = 91.45 [41] 2019 DEAP 32 Subjects Normalization, PSD 2D-CNN Softmax NA Acc = 88.76 [42] 2020 Clinical Different Subjects ICA 1D-CNN Softmax NA Acc = 92.44 [43] 2019 DEAP 32 Subjects k-means ECNNs Plurality voting NA Acc = 82.92 [44] 2020 DEAP 32 Subjects 3D EEG stream representation based on spatio-temporal information 3D-CNN NA PyTorch Acc-val = 99.11(2- class) Acc-aro = 99.74(2- class) Acc = 99.73(4-class) [45] 2018 SEED, DREAMER 15 (SEED), 23 (DREAMER) Adjacency matrix, Different features DGCNN Softmax NA Acc = 79.95(subject independent) Acc = 90.4(subject dependent) [46] 2019 SEED, DEAP 15 (SEED), 32 (DEAP) DE, PLV connectivity GCCN Softmax NA Acc = 84.35 [47] 2019 SEED, DREAMER 15 (SEED), 23 (DREAMER) DE, PSD, DASM, RASM, DCAU, LDS, BLS GCB-net Softmax NA Acc = 94.24 [48] 2019 DEAP 32 Subjects Normalization Multi-column CNN Voting Pytorch Acc-val = 90.01 Acc-aro = 90.65 [49] 2020 DEAP 32 Subjects Brain connectivity, PCC, PLV, TE 2D-CNN Softmax PyTorch.

    Periodic Counter-current Chromatography:

    Article Title: Emotion recognition in EEG signals using deep learning methods: A review.
    Article Snippet: Works Year Dataset Number of Cases Preprocessing DL Model Classifier Toolbox Performance Ceria (%) [29] 2018 SEED 15 Subjects DE HCNN NA MATLAB Acc (Beta) = 86.2 Acc(Gamma) = 88.2 [30] 2021 SEED, DEAP 15 (SEED), 32 (DEAP) CWT 2D-CNN Softmax MATLAB Acc = 49.12 (3-class) Acc = 54 (2-class) [31] 2023 DEAP 32 Subjects time, frequency and timefrequency features 2D-CNN NA PyTorch Acc-val = 98.36 Acc-aro = 98.27 Acc = 98.38(4-class) [32] 2022 DEAP 32 Subjects 3D spatial-spectral features DBCN Softmax NA Acc-val = 90.93 (subject-dependent) Acc-aro = 89.67 (subject-dependent) Acc-val = 83.98 (subject-independent) Acc-aro = 79.45 (subject-independent) [33] 2018 DEAP 32 Subjects Down sampling, STFT 2D-CNN Softmax TensorFlow Acc-aro = 76.56 Acc-val = 80.46 [34] 2019 DEAP 32 Subjects MFM CapsNet NA TensorFlow Acc-aro = 68.28 Acc-val = 66.73 Acc-dom = 67.25 [35] 2019 DEAP, SEED 15 (SEED), 32 (DEAP) Spectrogram, BoDF AlexNet SVM NA Acc = 93.8 [36] 2019 DREAMER, AMIGOS, MAHNOB-HCI, DEAP Different Subjects Frequency filters, DE, PSE, RGB heat-map, PCA VGG16 for feature extraction, LSTM ELM NA Acc = 81.05 [37] 2020 DEAP 32 Subjects 3D multiscale sample entropy matrix 2D-CNN HMM TensorFlow Acc-val = 79.77 Acc-aro = 83.09 Acc-dom = 81.83 [38] 2021 DEAP, SEED, DREAMER, AMIGOS 32 Subjects Topographic and holographic feature maps 2D-CNN SVM MATLAB Acc = 89.31 [39] 2021 DEAP 32 Subjects STFT, DA algorithm Borderline-SMOTE 1D-CNN Softmax NA Acc-val = 97.47 Acc-aro = 97.76 [40] 2021 SEED 15 Subjects CWT, DE 2D-CNN Softmax Python Acc = 91.45 [41] 2019 DEAP 32 Subjects Normalization, PSD 2D-CNN Softmax NA Acc = 88.76 [42] 2020 Clinical Different Subjects ICA 1D-CNN Softmax NA Acc = 92.44 [43] 2019 DEAP 32 Subjects k-means ECNNs Plurality voting NA Acc = 82.92 [44] 2020 DEAP 32 Subjects 3D EEG stream representation based on spatio-temporal information 3D-CNN NA PyTorch Acc-val = 99.11(2- class) Acc-aro = 99.74(2- class) Acc = 99.73(4-class) [45] 2018 SEED, DREAMER 15 (SEED), 23 (DREAMER) Adjacency matrix, Different features DGCNN Softmax NA Acc = 79.95(subject independent) Acc = 90.4(subject dependent) [46] 2019 SEED, DEAP 15 (SEED), 32 (DEAP) DE, PLV connectivity GCCN Softmax NA Acc = 84.35 [47] 2019 SEED, DREAMER 15 (SEED), 23 (DREAMER) DE, PSD, DASM, RASM, DCAU, LDS, BLS GCB-net Softmax NA Acc = 94.24 [48] 2019 DEAP 32 Subjects Normalization Multi-column CNN Voting Pytorch Acc-val = 90.01 Acc-aro = 90.65 [49] 2020 DEAP 32 Subjects Brain connectivity, PCC, PLV, TE 2D-CNN Softmax PyTorch.



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    gcb-net  (SoftMax Inc)
    90
    SoftMax Inc gcb-net
    Gcb Net, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gcb-net/gcb+net/pm37708717-246-366-368
    Average 90 stars, based on 1 article reviews
    gcb-net - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

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