scholarly journals Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

2019 ◽  
Author(s):  
Abhijith Mundanad Narayanan ◽  
Alexander Bertrand
Keyword(s):  
Sensor Networks ◽  
Channel Selection ◽  
Auditory Attention ◽  
Sensor Nodes ◽  
Selection Strategy ◽  
Long Distance ◽  
Selection Strategies ◽  
Eeg Data ◽  
Eeg Recordings

AbstractObjectiveConcealable, miniaturized electroencephalo-graphy (‘mini-EEG’) recording devices are crucial enablers towards long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm.MethodsStarting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes which locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes, to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings.ResultsThe AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if in both cases the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes.ConclusionWhen the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD.SignificanceWESN-like platforms allow to achieve similar AAD performance as with long-distance EEG recordings, while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.

scholarly journals Magnetic Induction Technology-Based Wireless Sensor Network for Underground Infrastructure, Monitoring Soil Conditions, and Environmental Observation Applications: Challenges and Future Aspects

2022 ◽  
Vol 2022 ◽  
pp. 1-18
Author(s):  
Pratap Singh ◽  
Rishi Pal Singh ◽  
Yudhvir Singh ◽  
Jasgurpreet Singh Chohan ◽  
Shubham Sharma ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Magnetic Induction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Soil Conditions ◽  
Long Distance ◽  
Infrastructure Monitoring ◽  
Research Areas ◽  
Underground Infrastructure ◽  
Type Water

Wireless sensor networks (WSNs) especially with sensor nodes communicating with each other in medium other than air have been naive area of research since the last few years. In comparison to underwater communication, wireless underground sensor networks (WUSNs) are now being used in a large number of applications ranging from environmental observation, estimating chances of earthquake, communicating in underground tunnels or mines, and infrastructure monitoring to soil monitoring for agricultural purposes. In spite of all such promising applications, due to harsh and dynamically changing soil characteristics including soil type, water content in soil, and soil temperature, underground communication with conventional electromagnetic (EM) wave-based technology could not prove to be feasible for long-distance communication. Alternatively, due to magnetic permeability of soil being similar to air, magnetic induction- (MI-) based approach was adopted using magnetic coils as antenna for sensor nodes. Subsequently, MI waveguide and 3D coil mechanisms were considered to improve the system efficiency. Attributing to different characteristics of underlying transmission channels, communication protocols as well as architecture of MI-based WUSNS (MI-WUSNs) have been developed with different approaches. In this review paper, in addition to the latest advancements made for MI-WUSNs, closely associated areas of MI-WUSNs have also been explored. Additionally, research areas which are still open to be worked upon have been detailed out.

scholarly journals Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246769 ◽  
Author(s):  
Jair Montoya-Martínez ◽  
Jonas Vanthornhout ◽  
Alexander Bertrand ◽  
Tom Francart
Keyword(s):  
Reconstruction Error ◽  
Optimal Number ◽  
Selection Strategy ◽  
Practical Applications ◽  
Auditory Neuroscience ◽  
Eeg Data ◽  
Subject Specific ◽  
Independent Case ◽  
The Subject ◽  
Eeg Recordings

Measurement of neural tracking of natural running speech from the electroencephalogram (EEG) is an increasingly popular method in auditory neuroscience and has applications in audiology. The method involves decoding the envelope of the speech signal from the EEG signal, and calculating the correlation with the envelope of the audio stream that was presented to the subject. Typically EEG systems with 64 or more electrodes are used. However, in practical applications, set-ups with fewer electrodes are required. Here, we determine the optimal number of electrodes, and the best position to place a limited number of electrodes on the scalp. We propose a channel selection strategy based on an utility metric, which allows a quick quantitative assessment of the influence of a channel (or a group of channels) on the reconstruction error. We consider two use cases: a subject-specific case, where the optimal number and position of the electrodes is determined for each subject individually, and a subject-independent case, where the electrodes are placed at the same positions (in the 10-20 system) for all the subjects. We evaluated our approach using 64-channel EEG data from 90 subjects. In the subject-specific case we found that the correlation between actual and reconstructed envelope first increased with decreasing number of electrodes, with an optimum at around 20 electrodes, yielding 29% higher correlations using the optimal number of electrodes compared to all electrodes. This means that our strategy of removing electrodes can be used to improve the correlation metric in high-density EEG recordings. In the subject-independent case, we obtained a stable decoding performance when decreasing from 64 to 22 channels. When the number of channels was further decreased, the correlation decreased. For a maximal decrease in correlation of 10%, 32 well-placed electrodes were sufficient in 91% of the subjects.

scholarly journals Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

2019 ◽  
Author(s):  
Jair Montoya-Martínez ◽  
Alexander Bertrand ◽  
Tom Francart
Keyword(s):  
Reconstruction Error ◽  
Optimal Number ◽  
Selection Strategy ◽  
Practical Applications ◽  
Auditory Neuroscience ◽  
Eeg Data ◽  
Subject Specific ◽  
Independent Case ◽  
The Subject ◽  
Eeg Recordings

AbstractMeasurement of neural tracking of natural running speech from the electroencephalogram (EEG) is an increasingly popular method in auditory neuroscience and has applications in audiology. The method involves decoding the envelope of the speech signal from the EEG signal, and calculating the correlation with the envelope of the audio stream that was presented to the subject. Typically EEG systems with 64 or more electrodes are used. However, in practical applications, set-ups with fewer electrodes are required. Here, we determine the optimal number of electrodes, and the best position to place a limited number of electrodes on the scalp. We propose a channel selection strategy based on an utility metric, which allows a quick quantitative assessment of the influence of a channel (or a group of channels) on the reconstruction error. We consider two use cases: a subject-specific case, where the optimal number and position of the electrodes is determined for each subject individually, and a subject-independent case, where the electrodes are placed at the same positions (in the 10-20 system) for all the subjects. We evaluated our approach using 64-channel EEG data from 90 subjects. In the subject-specific case we found that the correlation between actual and reconstructed envelope first increased with decreasing number of electrodes, with an optimum at around 20 electrodes, yielding 29% higher correlations using the optimal number of electrodes compared to all electrodes. This means that our strategy of removing electrodes can be used to improve the correlation metric in high-density EEG recordings. In the subject-independent case, we obtained a stable decoding performance when decreasing from 64 to 22 channels. When the number of channels was further decreased, the correlation decreased. For a maximal decrease in correlation of 10%, 32 well-placed electrodes were sufficient in 91% of the subjects.

scholarly journals Mobile ear-EEG to study auditory attention in everyday life

2020 ◽  
Author(s):  
Daniel Hölle ◽  
Joost Meekes ◽  
Martin G. Bleichner
Keyword(s):  
Everyday Life ◽  
Auditory Perception ◽  
Reaction Times ◽  
Auditory Attention ◽  
Acoustic Environment ◽  
Movement Data ◽  
Eeg Data ◽  
P3 Component ◽  
Mobile Smartphone

AbstractMost research investigating auditory perception is conducted in controlled laboratory settings, potentially restricting its generalizability to the complex acoustic environment outside the lab. The present study, in contrast, investigated auditory attention with long-term recordings (>6 h) beyond the lab using a fully mobile, smartphone-based ear-centered electroencephalography (EEG) setup with minimal restrictions for participants. Twelve participants completed iterations of two variants of an oddball task where they had to react to target tones and to ignore standard tones. A rapid variant of the task (tones every 2 seconds, 5 minutes total time) was performed seated and with full focus in the morning, around noon and in the afternoon under controlled conditions. A sporadic variant (tones every minute, 160 minutes total time) was performed once in the morning and once in the afternoon while participants followed their normal office day routine. EEG data, behavioural data, and movement data (with a gyroscope) were recorded and analyzed. The expected increased amplitude of the P3 component in response to the target tone was observed for both the rapid and the sporadic oddball. Miss rates were lower and reaction times were faster in the rapid oddball compared to the sporadic one. The movement data indicated that participants spent most of their office day at relative rest. Overall, this study demonstrated that it is feasible to study auditory perception in everyday life with long-term ear-EEG.

scholarly journals Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Chong Li ◽  
Tianyu Jia ◽  
Quan Xu ◽  
Linhong Ji ◽  
Yu Pan
Keyword(s):  
Closed Loop ◽  
Tracking System ◽  
Channel Selection ◽  
Brain Regions ◽  
Computer Interface ◽  
Selection Strategy ◽  
Motor Rehabilitation ◽  
Stroke Patients ◽  
Selection Strategies ◽  
Motor Intention

It is very important for the corresponding author to have a linked ORCID (Open Researcher and Contributor ID) account on MTS. To register a linked ORCID account, please go to the Account Update page (http://mts.hindawi.com/update/) in our Manuscript Tracking System and after you have logged in click on the ORCID link at the top of the page. This link will take you to the ORCID website where you will be able to create an account for yourself. Once you have done so, your new ORCID will be saved in our Manuscript Tracking System automatically.”"?>In the last decade, technology-assisted stroke rehabilitation has been the focus of research. Electroencephalogram- (EEG-) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients since the closed loop between motor intention and the actual movement established by BCI can stimulate the neural pathways of motor control. Due to the deficits in the brain, motor intention expression may shift to other brain regions during and even after neural reorganization. The objective of this paper was to study the event-related desynchronization (ERD) topography during motor attempt tasks of the paretic hand in stroke patients and compare the classification performance using different channel-selection strategies in EEG-based BCI. Fifteen stroke patients were recruited in this study. A cue-based experimental paradigm was applied in the experiment, in which each patient was required to open the palm of the paretic or the unaffected hand. EEG was recorded and analyzed to measure the motor intention and indicate the activated brain regions. Support vector machine (SVM) combined with common spatial pattern (CSP) algorithm was used to calculate the offline classification accuracy between the motor attempt of the paretic hand and the resting state applying different channel-selection strategies. Results showed individualized ERD topography during the motor attempt of the paretic hand due to the deficits caused by stroke. Statistical analysis showed a significant increase in the classification accuracy by analyzing the channels showing ERD than analyzing the channels from the contralateral sensorimotor cortex (SM1). The results indicated that for stroke patients whose affected motor cortex is extensively damaged, the compensated brain regions should be considered for implementing EEG-based BCI for motor rehabilitation as the closed loop between the altered activated brain regions and the paretic hand can be stimulated more accurately using the individualized channel-selection strategy.

scholarly journals The effects of LT-SN on energy dissipation and lifetime in wireless sensor networks

2016 ◽  
Vol 7 (1) ◽  
pp. 50-58
Author(s):  
Zeydin Pala
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Low Cost ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Security Vulnerabilities ◽  
Waking Up

Wireless sensor networks (WSNs) still attract the attention of researchers, users and the private sector despite their low power and low range tendency for malfunction. This attraction towards WSNs results from their low cost structure and the solutions they offer for many prevalent problems. Many conditions, which remain unforeseen or unexpected during the design of the system, may arise after the initialization of the system. Similarly, many situations where security vulnerabilities take place may emerge in time in WSNs operating normally. In this study, we called nodes which enter sleeping mode without any further waking up and causing a sparser number of nodes in the network without any function in data transmission as Long-Term Sleep Nodes (LT-SN); and considered energy spaces caused by such nodes as a problem; and established two Linear Programming (LP) models based on the efficiency of the present nodes. We offered two different models which present the effect of sensor nodes, which were initially operating in wireless sensor network environment and did not wake up following sleep mode, on network lifetime. The results of the present study report that as the number of LT-SN increases, the lifetime of the network decreases.

scholarly journals Preoperative Electroencephalographic Investigation in Temporal Lobe Epilepsy: Extracranial and Intracranial Recordings

1991 ◽  
Vol 18 (S4) ◽  
pp. 554-558 ◽  
Author(s):  
Pierre Gloor
Keyword(s):  
Temporal Lobe ◽  
Video Monitoring ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Data ◽  
Temporal Lobe Seizures ◽  
Eeg Recordings ◽  
Intracranial Recordings ◽  
Eeg Features

ABSTRACT:Preoperative EEG investigations of patients with temporal lobe seizures include extracranial interictal and ictal recordings during wakefulness and sleep, including long-term EEG and video-monitoring. Interictal epileptiform discharges when evaluated conservatively and in conjunction with other EEG and non-EEG localizing information, provide valuable guidance for the identification of the area to be resected, as do ictal recordings. When extracranial EEG features in conjunction with non-EEG data provide conflicting localizing information, intracranial recordings with stereotaxically implanted depth and epidural electrodes are used. Intracranial recordings must be designed to avoid biasing the exploration strategy in favor of one's preferred localizing hypothesis. Patients with evidence for bitemporal epileptogenic dysfunction in extracranial EEG recordings are suitable candidates for intracranial recordings. The majority of the patients explored in this manner show that all or more than 80% of their seizures arise from one temporal lobe. Excision of that lobe yields satisfactory results in a fair proportion of these patients. The number of satisfactory outcomes is, however, still somewhat less than in patients with unilateral temporal foci in extracranial EEG recordings.

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