Bayesian network based heuristic for energy aware EEG signal classification
الملخص
A major challenge in the current research of wireless electroencephalograph (EEG) sensor-based medical or Brain Computer Interface applications is how to classify EEG signals as accurately and energy efficient as possible. One way to achieve this objective is to select a subset of the most discriminant EEG channels during the signal classification. In this paper, we propose a Bayesian network based-heuristic channel selection approach. First, the EEG channels are ranked based on their task discriminant capabilities. The highest task-related channels are chosen as an initial set. Subsequently, this set is submitted to a Bayesian network to calculate the task weights. Based on these weights, the heuristic algorithm is either selects an appropriate channel or ends the selection process. The proposed technique has been applied on two classification problems. It achieved 92% and 93.39% classification accuracies, utilizing only 6 out of 14 channels and 13 out of 64 channels, respectively. Springer International Publishing 2013.
المجموعات
- علوم وهندسة الحاسب [2284 items ]
وثائق ذات صلة
عرض الوثائق المتصلة بواسطة: العنوان، المؤلف، المنشئ والموضوع.
-
Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks
Ahishali M.; Kiranyaz, Mustafa Serkan; Ince T.; Gabbouj M. ( Institute of Electrical and Electronics Engineers Inc. , 2020 , Conference Paper)In this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach ... -
How divided is a cell? Eigenphase nuclei for classification of mitotic phase in cancer histology images
Awan, Ruqayya; Aloraidi, Nada; Qidwai, Uvais; Rajpoot, Nasir ( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)Detection of mitotic cells in histology images is an important but challenging process due to the resemblance of mitotic cells with other non-mitotic cells and also due to the different appearance of mitotic cells undergoing ... -
Performance Comparison of Learned vs. Engineered Features for Polarimetric SAR Terrain Classification
Ahishali M.; Ince T.; Kiranyaz, Mustafa Serkan; Gabbouj M. ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)In this work, we propose to use learned features for terrain classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. In the proposed classification framework, the learned features are extracted from sliding ...