Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network

Pradeepkumar, Jathurshan; Anandakumar, Mithunjha; Vinith, Kugathasan,; Lalitharatne, Thilina Dulantha; Silva, Anjula C. De; Kappel, Simon L.

doi:10.1109/embc46164.2021.9630958

Cited by 3 publications

(2 citation statements)

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“…Pradeepkumar et al [20] proposed a hand gesture recognition system utilizing ECoG signals and tested the recognition performance using the finger flex dataset [21], which contains ECoG recordings of three anonymous patients with corresponding finger movement information. Using feature reduction based on statistical analysis and an LSTM neural network, their system achieved a classification accuracy of 82.4%.…”

Section: Related Workmentioning

confidence: 99%

Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map

Kawaguchi,

Ono,

Hikawa

2024

Sensors

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Brain–computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain’s status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α, β, and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.

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Section: Related Workmentioning

confidence: 99%

Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map

Kawaguchi,

Ono,

Hikawa

2024

Sensors

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“…The extraction of relevant information from EEG recordings using deep Neural Networks (NN)s has demonstrated promising results in detecting neurological illnesses such as epilepsy [2], [3] and recognizing ischemic stroke [4], identifying sleep disorders [5], decoding motor imagery tasks [6], [7], as well as hand movement preparation stages [8], and supporting stroke rehabilitation via Brain-Computer Interface (BCI) [9]. In addition, the extraction of useful information from ECoG recordings using deep NNs has been proven very effective in imagined 3D continuous hand translation [10], decoding the finger flexion [11], [12] recognizing the state of behavioral sleep and waking state [13], decoding hand gestures [14], [15], and detecting speech activity [16]. All the above works extract features from the raw EEG or ECoG signals using DL, most usually Convolutional Neural Networks (CNN)s, and then harness a classification or regression model to make predictions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing DNN Models for EEG/ECoG BCI With a Novel Data-Driven Offline Optimization Method

2023

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A better understanding of ElectroEncephaloGraphy (EEG) and ElectroCorticoGram (ECoG) signals would get us closer to comprehending brain functionality, creating new avenues for treating brain abnormalities and developing novel Brain-Computer Interface (BCI)-related applications. Deep Neural Networks (DNN)s have lately been employed with remarkable success to decode EEG/ECoG signals for BCI. However, the optimal architectural/training parameter values in these DNN architectures have yet to receive much attention. In addition, new data-driven optimization methodologies that leverage significant advancements in Machine Learning, such as the Transformer model, have recently been proposed. Because an exhaustive search on all possible architectural/training parameter values of the state-of-the-art DNN model (our baseline model) decoding the motor imagery EEG and finger tension ECoG signals comprising the BCI IV 2a and 4 datasets, respectively, would require prohibitively much time, this paper proposes an offline model-based optimization technique based on the Transformer model for the discovery of the optimal architectural/training parameter values for that model. Our findings indicate that we could pick better values for the baseline model's architectural/training parameters, enhancing the baseline model's performance by up to 14.7% in the BCI IV 2a dataset and by up to 61.0% in the BCI IV 4 dataset.

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