The paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. Related research work in the field of imagined speech has revealed that imagined speech decoding performance and accuracy require attention to further improve. The evolution of deep learning technology increases the likelihood of decoding imagined speech from EEG signals with enhanced performance. We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. The experiment was carried out using an open-access dataset of fifteen subjects’ imagined speech multichannel signals of vowels and words. The raw multichannel EEG signals of multiple subjects were processed using discrete wavelet transformation technique. The model was trained and evaluated using the preprocessed signals, and the model hyperparameters were adjusted to achieve higher accuracy in the classification of imagined speech. The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. The results of the study indicate that individuals with speech difficulties might well be able to leverage a noninvasive EEG-based imagined speech brain-computer interface system as one of the long-term alternative artificial verbal communication mediums.
Objective. In recent years, imagined speech brain-computer (machine) interface applications have been an important field of study that can improve the lives of patients with speech problems through alternative verbal communication. This study aimed to classify the imagined speech of numerical digits from electroencephalography (EEG) signals by exploiting the past and future temporal characteristics of the signal using several deep learning models. Approach. This study proposed a methodological combination of EEG signal processing techniques and deep learning models for the recognition of imagined speech signals. The EEG signals were filtered and preprocessed using the discrete wavelet transformation (DWT) to remove artifacts and retrieve feature information. To classify the preprocessed imagined speech neural signals, multiple versions of multlayer bidirectional recurrent neural networks were used. Main results. The method was examined by leveraging MUSE and EPOC signals from MNIST imagined digits in the MindBigData open-access database. The presented methodology’s classification performance accuracy was noteworthy, with the model’s multiclass overall classification accuracy reaching a maximum of 96.18 percent on MUSE signals and 71.60 percent on EPOC signals. Significance. Furthermore, this research shows that the proposed signal preprocessing approach and the stacked bidirectional recurrent network model are suitable for extracting the high temporal resolution of EEG signals in order to classify imagined digits, indicating the unique neural identity of each imagined digit class that distinguished it from the others.
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