Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

Chan, Alexander; Early, C.; Subedi, S.; Li, Yuezhe; Lin, Hong

doi:10.1109/bibm.2015.7359788

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Previous studies [24][25][26] have shown that EEG spectral features such as the band power coefficients are correlated to the cognitive workload. Therefore, this representation was used to train our recurrent model.…”

Section: Eeg Frequency Featuresmentioning

confidence: 99%

A Neurotechnological Aid For Semi-Autonomous Suction In Robotic-Assisted Surgery

Barragan¹,

Yang²,

Yu³

et al. 2021

Preprint

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Adoption of Robotic-Assisted Surgery has steadily increased as it improves the surgeon’s dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant’s tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.

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Section: Eeg Frequency Featuresmentioning

confidence: 99%

A Neurotechnological Aid For Semi-Autonomous Suction In Robotic-Assisted Surgery

Barragan¹,

Yang²,

Yu³

et al. 2021

Preprint

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“…Before the popularity of deep learning, the primary approaches for feature extraction mainly included time-frequency features extracted by signal analysis methods, such as power spectral density [42], bandpower [43], independent components [44], and differential entropy [45]. The widely researched pattern recognition and machine learning methods include artificial neural networks [46, 47], naive Bayes [48], support vector machines (SVM) [49, 50], etc. With the extensive application and in-depth promotion of deep learning, an ever-increasing number of brain science and neuroscience research teams are exploiting its strength in designing algorithms to achieve intelligent understanding and analysis of brain activities via EEGs, leading to propose an end-to-end model by integrating feature extraction and classification/clustering.…”

Section: Related Workmentioning

confidence: 99%

EEG-based image classification via a region-level stacked bi-directional deep learning framework

Fares

Zhong

Jiang

2019

BMC Med Inform Decis Mak

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BackgroundAs a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid development of machine learning and artificial intelligence, the analysis and calculation of EEGs has made great progress, leading to a significant boost in performances for content understanding and pattern recognition of brain activities across the areas of both neural science and computer vision. While such an enormous advance has attracted wide range of interests among relevant research communities, EEG-based classification of brain activities evoked by images still demands efforts for further improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored.MethodsWe propose a region-level stacked bi-directional deep learning framework for EEG-based image classification. Inspired by the hemispheric lateralization of human brains, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The stacked bi-directional long short-term memories are used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequences.ResultsExtensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing state-of-the-arts, our framework achieves outstanding performances in EEG-based classification of brain activities evoked by images. In addition, we find that the signals of Gamma band are not only useful for achieving good performances for EEG-based image classification, but also play a significant role in capturing relationships between the neural activations and the specific emotional states.ConclusionsOur proposed framework provides an improved solution for the problem that, given an image used to stimulate brain activities, we should be able to identify which class the stimuli image comes from by analyzing the EEG signals. The region-level information is extracted to preserve and emphasize the hemispheric lateralization for neural functions or cognitive processes of human brains. Further, stacked bi-directional LSTMs are used to capture the dynamic correlations hidden in EEG data. Extensive experiments on standard EEG-based image classification dataset validate that our framework outperforms the existing state-of-the-arts under various contexts and experimental setups.

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“…The extracted features are classified using three standard supervised classifiers, namely, support vector machine (SVM) (Mitchell 1997;Webb 2003;Zhang et al 2016), naive Bayes (NB) (Mitchell 1997;Leung 2007;Bhaduri et al 2016;Chan et al 2015) and k-nearest neighbor (kNN) (Mitchell 1997;Page et al 2015) independent of each other. Classification is performed in a hierarchial one-vs.one (OVO) approach and majority voting (Paul et al 2006) is used to decide the final outcome.…”

Section: Classificationmentioning

confidence: 99%

Analyzing text recognition from tactually evoked EEG

Khasnobish

Datta

Bose³

et al. 2017

Cogn Neurodyn

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Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts. The pre-trained classifiers are used to classify the EEG data to identify the texts online and the recognized text is displayed on the computer screen for communication. Online classifications of two, four, and six classes of embossed texts are achieved with overall average recognition rates of 76.62, 72.31, and 67.62% respectively and the computational time is less than 2 s in each case. The maximum information transfer rate and utility of the system performance over all experiments are 0.7187 and 2.0529 bits/s respectively. This work presents a study that shows the possibility to classify 3D letters using tactually evoked EEG. In future, it will help the BCI community to design stimuli for better tactile augmentation n also opens new directions of research to facilitate 3D letters for visually impaired persons. Further, 3D maps can be generated for aiding tactual BCI in teleoperation.

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Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

Cited by 17 publications

References 7 publications

A Neurotechnological Aid For Semi-Autonomous Suction In Robotic-Assisted Surgery

A Neurotechnological Aid For Semi-Autonomous Suction In Robotic-Assisted Surgery

EEG-based image classification via a region-level stacked bi-directional deep learning framework

Analyzing text recognition from tactually evoked EEG

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