Bayesian Network Based Heuristic for Energy Aware EEG Signal Classification

Mohamed, Abduljalil; Shaban, Khaled; Mohamed, Amr

doi:10.1007/978-3-319-02753-1_25

Cited by 2 publications

(4 citation statements)

References 11 publications

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“…The nodes of the last layer are all connected to a final destination node (dummy node), and the nodes of the first layer are merged to constitute the root node of the directed graph. The graph construction process can be better explained by the following example attained from previous work [16]. The 14-channel Emotiv EPOC EEG sensor was used for the purpose of classification of three-mental tasks (T1, T2, T3).…”

Section: B Constructing Directed Graphmentioning

confidence: 99%

“…Using a 14-channel EEG sensor, signals were collected from five male subjects, referred to as S1, S2, S3, S4, and S5. The data collection procedure outlined in [16] was used. Each subject was asked to perform three mental tasks namely, sending an email, dialing a phone number, and initiating Skype session.…”

Section: A Data Collectionmentioning

confidence: 99%

“…Each subject was asked to perform three mental tasks namely, sending an email, dialing a phone number, and initiating Skype session. Interested readers should refer to [16] for more details.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Directed graph-based wireless EEG sensor channel selection approach for cognitive task classification

Mohamed

Shaban

Mohamed

2016

2016 International Wireless Communications and Mobile Computing Conference (IWCMC)

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Abstract-Wireless electroencephalogram (EEG) sensors havebeen successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they are low powered devices, and hence an efficient usage of sensor energy resources is critical for any practical application. One way of minimizing energy consumption by the EEG sensors is by reducing the number of EEG channels participating in the classification process. For the purpose of classifying EEG signals, we propose a directed acyclic graph (DAG)-based channel selection algorithm. To achieve this objective, the EEG sensor channels are first realized in a complete undirected graph, where each channel is represented by a node. An edge between any two nodes indicates the collaboration between these nodes in identifying the system state; and the significance of this collaboration is quantified by a weight assigned to the edge. The complete graph is then reduced into a directed acyclic graph that encodes the knowledge of the non-increasing order of the channel ranking for each cognitive task. The channel selection algorithm utilizes this directed graph to find a maximum path such that the total weight of this path satisfies a predefined threshold. It has been demonstrated experimentally that channel utilization has been reduced by 50% in the worst case scenario for a three-state system and an EEG sensor with 14 channels; and the best classification accuracy obtained is 81%.

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Section: B Constructing Directed Graphmentioning

confidence: 99%

Section: A Data Collectionmentioning

confidence: 99%

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Directed graph-based wireless EEG sensor channel selection approach for cognitive task classification

Mohamed

Shaban

Mohamed

2016

2016 International Wireless Communications and Mobile Computing Conference (IWCMC)

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“…This thesis focused onto this idea, which mixes the art with the science, to produce a textile hanging that has the capability to resist microbes including bacteria and viruses specially corona virus, without any side effect or any distortion to the design [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%