A review of EEG based human facial expression recognition systems in cognitive sciences

Buvaneswari, B.; Reddy, T. Kalpalatha

doi:10.1109/icecds.2017.8390209

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Its various bands can reflect the internal activity state of the brain ( Table 2 ). EEGs have been widely used in emotion recognition [ 31 , 32 ], attention level measurement [ 33 ], cognitive workload measurement [ 34 , 35 ], thinking-state detection [ 36 , 37 ], academic stress detection [ 38 ], cognitive psychological disease detection [ 39 , 40 ], fatigue monitoring [ 41 ], mind control [ 42 ], and other areas. Since the biological nature of EEG information is difficult to disguise or mask, EEGs can more objectively reflect internal processes than behaviors, voices, facial expressions, and so on [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism

et al. 2021

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Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included labeling learners’ actual learning styles, designing a method to effectively stimulate different learners’ internal state differences regarding learning styles, designing the data-collection method, designing the preprocessing procedure, and constructing the recognition model. In this way, we designed and verified an experimental method that can effectively stimulate learning-style differences in the information-processing dimension. In addition, we verified the effectiveness of using EEG signals to recognize learning style. The recognition accuracy of the learning-style processing dimension was 71.2%. This result is highly significant for the further exploration of using EEG signals for effective learning-style recognition.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism

et al. 2021

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“…The characteristics of EEG signals have been widely used for emotion recognition [ 33 , 34 ]; measuring attention levels [ 35 ] and cognitive load [ 36 , 37 ]; detecting states of cognition [ 38 , 39 ], academic stress [ 40 ], cognitive mental diseases [ 41 , 42 ], motor imagery [ 43 , 44 ], and music preference [ 45 ]; fatigue monitoring [ 46 ] and mind control [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals

et al. 2021

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Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people’s learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people’s learning styles.

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