Decoding complex imagery hand gestures

Salehi, Seyed Sadegh Mohseni; Moghadamfalahi, Mohammad; Quivira, Fernando; Piers, Alexander; Nezamfar, Hooman; Erdoğmuş, Deni̇z

doi:10.1109/embc.2017.8037480

Cited by 7 publications

(2 citation statements)

References 14 publications

(15 reference statements)

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“…In the early stage of the study, most researchers focus on the fine movement of one single joint. For examples, Salehi et al 17 and Alazrai et al 18 did some research on motor imagery recognition of figure gestures. Vuckovic and Sepulveda utilized Gabor transform features for decoding four wrist movements (flexion, extension, pronation, and supination) 19 .…”

Section: Background and Summarymentioning

confidence: 99%

Multi-channel EEG recording during motor imagery of different joints from the same limb

Qiu

2020

Sci Data

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Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.

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Section: Background and Summarymentioning

confidence: 99%

Multi-channel EEG recording during motor imagery of different joints from the same limb

Qiu

2020

Sci Data

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“…Mohseni et al [14] proposed a motor imagery hand gesture decoding method that predicted intended hand grasps from EEG data. For motor imagery hand gestures, EEG signals are recorded while subjects are imagining the gesture.…”

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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Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces

Özdenizci

Gunay

Quivira

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.

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Decoding complex imagery hand gestures

Cited by 7 publications

References 14 publications

Multi-channel EEG recording during motor imagery of different joints from the same limb

Multi-channel EEG recording during motor imagery of different joints from the same limb

Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map

Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces

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