AlArfaj Abeer Abdulaziz, Hosni Mahmoud Hanan A, Hafez Alaaeldin M
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
Appl Bionics Biomech. 2022 Aug 5;2022:8645165. doi: 10.1155/2022/8645165. eCollection 2022.
Deep learning models are effectively employed to transfer learning to adopt learning from other areas. This research utilizes several neural structures to interpret the electroencephalogram images (EEG) of brain-injured cases to plan operative imagery-computerized interface models for controlling left and right hand movements. This research proposed a model parameter tuning with less training time using transfer learning techniques. The precision of the proposed model is assessed by the aptitudes of motor imagery detection. The experiments depict that the best performance is attained with the incorporation of the proposed EEG-DenseNet and the transfer model. The prediction accuracy of the model reached 96.5% with reduced time computational cost. These high performance proves that the EEG-DenseNet model has high prospective for motor imagery brain-injured therapy systems. It also productively exhibited the effectiveness of transfer learning techniques for enhancing the accuracy of electroencephalogram brain-injured therapy models.
深度学习模型被有效地用于迁移学习，以便从其他领域进行学习。本研究利用多种神经结构来解读脑损伤病例的脑电图图像（EEG），以规划用于控制左右手运动的手术图像计算机化接口模型。本研究提出了一种使用迁移学习技术且训练时间更短的模型参数调整方法。所提出模型的精度通过运动想象检测能力来评估。实验表明，结合所提出的EEG-DenseNet和迁移模型可获得最佳性能。该模型的预测准确率达到了96.5%，同时降低了计算成本。这些高性能证明了EEG-DenseNet模型在运动想象脑损伤治疗系统方面具有很高的前景。它还有效地展示了迁移学习技术在提高脑电图脑损伤治疗模型准确性方面的有效性。
