A Comparative Study: Use of a Brain-Computer Interface (BCI) Device by People with Cerebral Palsy in Interaction with Computers

Heidrich, Regina de Oliveira; Rebelo, Francisco; Branco, Marsal; Mossmann, João Batista; Schuh, Anderson; Jensen, Emely; Oliveira, Tiago

doi:10.1007/978-3-319-21383-5_68

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…e CPS using the brain-computer interface brought some alternate solutions for health issues of physically challenged and elderly persons as the BCI is an advanced technology that creates a human interaction with the externally available electronic and electromechanical equipment related to encoding and decoding of brain electrical signals. It is also helpful for humans having cerebral palsy, which is brain and muscle weakness or problems [18]. Many Russian researchers have conducted many experiments, and results revealed that the BCI based on EEG signals is much helpful in recognizing the human mental commands using the P300 waves.…”

Section: Literature Reviewmentioning

confidence: 99%

Human-in-the-Loop Predictive Analytics Using Statistical Learning

Ganesan

Paul

Nagabushnam

et al. 2021

Journal of Healthcare Engineering

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The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human’s input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human’s intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.

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Section: Literature Reviewmentioning

confidence: 99%

Human-in-the-Loop Predictive Analytics Using Statistical Learning

Ganesan

Paul

Nagabushnam

et al. 2021

Journal of Healthcare Engineering

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Deep-Learning Assisting Cerebral Palsy Patient Handgrip Task Translation

Zakaria

Warip

Ehkan

et al. 2021

J. Phys.: Conf. Ser.

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An electro-encephalography (EEG) brain-computer interface (BCI) can provide the brain and external environment with separate information sharing and control networks. EEG impulses, though, come from many electrodes, which produce different characteristics, and how the electrodes and features to enhance classification efficiency have been chosen has become an urgent concern. This paper explores the deep convolutional neural network architecture (CNN) hyper-parameters with separating temporal and spatial filters without any pre-processing or artificial extraction processes. It selects the raw EEG signal of electrode pairs over the cortical area as hybrid samples. Our proposed deep-learning model outperforms other neural network models previously applied to this dataset in training time (∼40%) and accuracy (∼6%). Besides, considerations such as optimum order for EEG channels do not limit our model, and it is patient-invariant. The impact of network architecture on decoder output and training time is further discussed.

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A Comparative Study: Use of a Brain-Computer Interface (BCI) Device by People with Cerebral Palsy in Interaction with Computers

Cited by 2 publications

References 3 publications

Human-in-the-Loop Predictive Analytics Using Statistical Learning

Human-in-the-Loop Predictive Analytics Using Statistical Learning

Deep-Learning Assisting Cerebral Palsy Patient Handgrip Task Translation

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