EEG Signal Processing: Theory and Applications

Thakor, Nitish V.; Sherman, David

doi:10.1007/978-1-4614-5227-0_5

Cited by 30 publications

(13 citation statements)

References 127 publications

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“…In order to determine the users' relaxation levels, we targeted brain waves of the alpha and theta frequency band. Alpha waves commonly occur when a person is awake, but in a calm and resting mental state, when the eyes are closed and with certain types of meditation [23], [24]. Theta waves can be observed while meditating, during daydream, state of flow or phases of drowsiness and hypnagogia [25].…”

Section: Eeg Headbandmentioning

confidence: 99%

Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG

Amores

Richer

Zhao

et al. 2018

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Section: Eeg Headbandmentioning

confidence: 99%

Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG

Amores

Richer

Zhao

et al. 2018

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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“…In a healthy brain, neuron activation potential is propagated along the axon to the nerve ending, where neurotransmitters are released. However, it is the synaptic potential that is the most important source of an electroencephalogram [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study

Pawuś

Paszkiel

2022

Applied Sciences

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This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot.

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“…In recent years, brain-computer interfacing (BCI) has achieved a plethora of academic achievements, initially focusing on medical applications whilst progressively expanding towards other applications [1]. Popular BCI paradigms for controlling devices are the steady-state evoked potentials (SSEPs) [2,3] event-related potentials (ERPs) [4], and event-related desynchronization/synchronization (ERD/ERS) [5], mostly in combination with electroencephalography (EEG) [6]. EEG recordings have also been used in passive BCI applications for task engagement [7][8][9] and mental workload monitoring [10], and for emotion recognition [11].…”

Section: Introductionmentioning

confidence: 99%

“…In [13], interest was defined as user experience and classified using EEG data of the entire video. Recent reports have shown the advantage of entropy-based methods of EEG analysis to discern emotional states, in particular the multivariate entropy ones as they are considered to better account for the temporal structure across channels [4,6,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Predicting Premature Video Skipping and Viewer Interest from EEG Recordings

Libert

Hulle

2019

Entropy

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Brain–computer interfacing has enjoyed growing attention, not only due to the stunning demonstrations with severely disabled patients, but also the advent of economically viable solutions in areas such as neuromarketing, mental state monitoring, and future human–machine interaction. An interesting case, at least for neuromarketers, is to monitor the customer’s mental state in response to watching a commercial. In this paper, as a novelty, we propose a method to predict from electroencephalography (EEG) recordings whether individuals decide to skip watching a video trailer. Based on multiscale sample entropy and signal power, indices were computed that gauge the viewer’s engagement and emotional affect. We then trained a support vector machine (SVM), a k-nearest neighbor (kNN), and a random forest (RF) classifier to predict whether the viewer declares interest in watching the video and whether he/she decides to skip it prematurely. Our model achieved an average single-subject classification accuracy of 75.803% for skipping and 73.3% for viewer interest for the SVM, 82.223% for skipping and 78.333% for viewer interest for the kNN, and 80.003% for skipping and 75.555% for interest for the RF. We conclude that EEG can provide indications of viewer interest and skipping behavior and provide directions for future research.

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EEG Signal Processing: Theory and Applications

Cited by 30 publications

References 127 publications

Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG

Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG

The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study

Predicting Premature Video Skipping and Viewer Interest from EEG Recordings

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