A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a dev...A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.展开更多
Brainwave is a kind of traceable neurophysiological energy in a living brain. Invisible to human eyes, it is only detectable using electroencephalography (EEG), electrocorticography (ECoG) and magnetoencephalography (...Brainwave is a kind of traceable neurophysiological energy in a living brain. Invisible to human eyes, it is only detectable using electroencephalography (EEG), electrocorticography (ECoG) and magnetoencephalography (MEG). The waves or oscillations or rhythms are produced mainly by the oscillatory networks of the brain. Three main oscillatory networks are thalamocortical, extrathalamic-cortical and cortical-cortical networks. Greater limbic system (reticular system, hypothalamus, thalamus, basal forebrain nuclei, limbic system) has a great influence on these oscillatory networks. This system which is in microgravity position lies deep inside and surrounded by the ventricles of the brain. It receives all information from inside and outside of our body and then projects to all areas of the brain (from all to one and from one to all—nearly similar concept to “from God back to God”). Therefore, the greater limbic system could be regarded as “a core of the neuroaxis” which lies in microgravity compartment and in microgravity position (“T”-shape or curving shape;whilst gravity position is “I”-shape or vertical shape). By knowing the origin of the brainwaves and methods to detect them, one may study seizure networks, normal and abnormal brain networks and arguably, even to explore the relationship between the “invisibles”: “invisible” brainwaves and “invisible” soul.展开更多
基金the SUT research and development fund,and in part by the National Natural Science Foundation of China under Grant 61771333All subjects gave their informed consent for inclusion before they participated in the study.The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Ethics Committee of Suranaree University of Technology(License EC-61-14 COA No.16/2561).
文摘A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.
文摘Brainwave is a kind of traceable neurophysiological energy in a living brain. Invisible to human eyes, it is only detectable using electroencephalography (EEG), electrocorticography (ECoG) and magnetoencephalography (MEG). The waves or oscillations or rhythms are produced mainly by the oscillatory networks of the brain. Three main oscillatory networks are thalamocortical, extrathalamic-cortical and cortical-cortical networks. Greater limbic system (reticular system, hypothalamus, thalamus, basal forebrain nuclei, limbic system) has a great influence on these oscillatory networks. This system which is in microgravity position lies deep inside and surrounded by the ventricles of the brain. It receives all information from inside and outside of our body and then projects to all areas of the brain (from all to one and from one to all—nearly similar concept to “from God back to God”). Therefore, the greater limbic system could be regarded as “a core of the neuroaxis” which lies in microgravity compartment and in microgravity position (“T”-shape or curving shape;whilst gravity position is “I”-shape or vertical shape). By knowing the origin of the brainwaves and methods to detect them, one may study seizure networks, normal and abnormal brain networks and arguably, even to explore the relationship between the “invisibles”: “invisible” brainwaves and “invisible” soul.
