CFP: IEEE CIM special issue on "Computational Intelligence for Brain Computer Interfaces"

IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence for Brain Computer Interfaces
http://www.husseinabbass.net/ieeecimbci2016.html
Hussein A. Abbass, Cuntai Guan, Kay Chen Tan

Aims and Scope

Brain Computer Interfaces (BCI) aims at establishing a one or two-way communication protocol between the human brain and an electronic device. The research umbrella of BCI has different names and overlaps with different research areas that evolved under the wider objective of connecting human data to an electronic device of some sort. Some of these areas include: adaptive automation, augmented cognition, brain-machine interface, human-machine symbiosis, and human-computer symbiosis.

The last decade has witnessed a rise in the number of researchers working on BCI. With the advances of sensor technologies, efficient signal processing algorithms, and parallel computing, it was possible to finally realize the dream of many researchers who talked about the concept in one form or another in the sixties and seventies including J.C.R. Licklider, R.B. Rouse, and others. Different sensor and measurement technologies are evolving rapidly from the classical functional magnetic resonance imaging (fMRI), functional near infrared (fNIR), Electroencephalography (EEG), to complex integrated psycho-physiological sensor arrays.

Researchers in Computational Intelligence have been better situated than ever to extract knowledge from these signals, transform it to actionable decisions, and designing the intelligent machine that has long been promised and is now overdue. Success has been seen in many medical applications including assisting people on wheelchairs, stroke rehabilitation, and epileptic seizures. In the non-medical domain, BCI has been used for computer games, authentication in cyber security, and air traffic control.

This special issue aims at showcasing the most exciting and recent advances in BCI and related topics. The guest editors invite submissions of previously unpublished, recent and exciting research on BCI. The special issue welcomes survey, position, and research papers

Topics of Interest

• Adaptive control schemes for BCI
• Applications
• Augmented cognition and adaptive aiding using BCI
• Big data for brain mining
• Collaborative multi-humans BCI environments
• Computational intelligence applications for BCI
• Data and signal processing techniques for BCI applications
• Evolutionary algorithms for BCI
• Fusion of heterogeneous psycho-physiological sensors
• Fuzzy logic for BCI
• Neuroplasticity induced by brain-computer interactions
• Neural networks for BCI
• Novel sensor technologies for BCI
• Related computational intelligence methods for BCI
• Situation awareness systems for BCI applications
• Swarm techniques for BCI
• Other closely related topics on computational intelligence for BCI

Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify on the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via https://www.easychair.org/conferences/?conf=ieeecimbci2016

Important Dates

15th May, 2015: Submission of Manuscripts
15th July, 2015: Notification of Review Results
15th August, 2015: Submission of Revised Manuscripts
15th September, 2015: Submission of Final Manuscripts
February 2016: Special Issue Publication

Guest Editors

Professor Hussein Abbass
University of New South Wales
Canberra, Northcott Drive, ACT 2600, Australia
Email: h.abbass@adfa.edu.au

Professor Cuntai Guan
Institute for Infocomm Research (I2R),
1 Fusionopolis Way, Fusionopolis, Singapore 138632
Email: ctguan@i2r.a-star.edu.sg

Professor Kay Chen Tan
National University of Singapore
4 Engineering Drive, Singapore 117583
Email: eletankc@nus.edu.sg

Call For Papers: IEEE CIM special issue on "Computational Intelligence for Changing Environments"

IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence for Changing Environments
Amir Hussain, Dacheng Tao, Jonathan Wu and Dongbin Zhao

Aims and Scope:

Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling.

To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non-stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments.

It is possible to use human-level processing power to tackle concept drift in changing enviroments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena.

However, a main outstanding issue in using computational intelligence for changing enviroments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing enviroments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments.

Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing enviroments algorithms and architectures, theoretical foundations, and representations, and their application to real-world problems. We also welcome timely surveys and review papers.

Topics of Interest include (but are not limited to):

• Computational intelligence methodologies and implementation for changing environments
• Transfer learning
• Multitask learning
• Domain adaption
• Incremental Learning architectures
• Incremental Unsupervised and semi-supervised learning architectures
• Incremental Incremental Representation learning and disentangling
• Incremental Knowledge augmentation
• Incremental Adaptive Neuro-fuzzy systems
• Incremental and single-pass data mining
• Incremental Neural Clustering
• Incremental Neural regression
• Incremental Adaptive decision systems
• Incremental Feature selection and reduction
• Incremental Constructive Learning
• Novelty detection in Incremental learning

Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via:

https://www.easychair.org/conferences/?conf=ieeecimcdbil2015.

Important Dates

1st Feb, 2015: Submission of Manuscripts
15th April, 2015: Notification of Review Results
15th May, 2015: Submission of Revised Manuscripts
15th June, 2015: Submission of Final Manuscripts
November 2015: Publication

Guest Editor

Professor Amir Hussain
University of Stirling
Stirling FK9 4LA SCOTLAND, UK
Email: ahu@cs.stir.ac.uk

Professor Dacheng Tao
University of Technology, Sydney
235 Jones Street, Ultimo, NSW 2007, Australia
Email: dacheng.tao@uts.edu.au

Professor Jonathan Wu
University of Windsor
401 Sunset Avenue, Windsor, ON, Canada
Email: jwu@uwindsor.ca

Professor Dongbin Zhao
Institute of Automation, Chinese Academy of Sciences,
No. 95, Zhongguancun East Road, Beijing 100190, China
E-mail: dongbin.zhao@gmail.com

IEEE Computational Intelligence Magazine Special Issue on "Computational Intelligence for Brain Computer Interfaces"

Aims and Scope

Brain Computer Interfaces (BCI) aims at establishing a one or two-way communication protocol between the human brain and an electronic device. The research umbrella of BCI has different names and overlaps with different research areas that evolved under the wider objective of connecting human data to an electronic device of some sort. Some of these areas include: adaptive automation, augmented cognition, brain-machine interface, human-machine symbiosis, and human-computer symbiosis.

The last decade has witnessed a rise in the number of researchers working on BCI. With the advances of sensor technologies, efficient signal processing algorithms, and parallel computing, it was possible to finally realize the dream of many researchers who talked about the concept in one form or another in the sixties and seventies including J.C.R. Licklider, R.B. Rouse, and others. Different sensor and measurement technologies are evolving rapidly from the classical functional magnetic resonance imaging (fMRI), functional near infrared (fNIR), Electroencephalography (EEG), to complex integrated psycho-physiological sensor arrays.

Researchers in Computational Intelligence have been better situated than ever to extract knowledge from these signals, transform it to actionable decisions, and designing the intelligent machine that has long been promised and is now overdue. Success has been seen in many medical applications including assisting people on wheelchairs, stroke rehabilitation, and epileptic seizures. In the non-medical domain, BCI has been used for computer games, authentication in cyber security, and air traffic control.

This special issue aims at showcasing the most exciting and recent advances in BCI and related topics. The guest editors invite submissions of previously unpublished, recent and exciting research on BCI. The special issue welcomes survey, position, and research papers

Topics of Interest include:

• Adaptive control schemes for BCI
• Applications
• Augmented cognition and adaptive aiding using BCI
• Big data for brain mining
• Collaborative multi-humans BCI environments
• Computational intelligence applications for BCI
• Data and signal processing techniques for BCI applications
• Evolutionary algorithms for BCI
• Fusion of heterogeneous psycho-physiological sensors
• Fuzzy logic for BCI
• Neuroplasticity induced by brain-computer interactions
• Neural networks for BCI
• Novel sensor technologies for BCI
• Related computational intelligence methods for BCI
• Situation awareness systems for BCI applications
• Swarm techniques for BCI
• Other closely related topics on computational intelligence for BCI

Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify on the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via https://www.easychair.org/conferences/?conf=ieeecimbci2016

Important Dates (for February 2016 Issue)

15th May, 2015: Submission of Manuscripts
15th July, 2015: Notification of Review Results
15th August, 2015: Submission of Revised Manuscripts
15th September, 2015: Submission of Final Manuscripts
February 2016: Special Issue Publication

Guest Editors

Hussein A. Abbass, The University of New South Wales, School of Engineering and Information Technology, Canberra, ACT 2600, Australia.
Cuntai Guan, Institute for Infocomm Research (I2R), 1 Fusionopolis Way, Fusionopolis, 138632, Singapore.
Kay Chen Tan, National University of Singapore, Department of Electrical and Computer Engineering, 4 Engineering Drive, 117583, Singapore.

CFP: Special Issue IEEE Computational Intelligence Magazine on "Computational Intelligence for Changing Environments"

Aims and Scope

Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling.

To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non-stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments.

It is possible to use human-level processing power to tackle concept drift in changing enviroments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena.

However, a main outstanding issue in using computational intelligence for changing enviroments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing enviroments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments.

Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing enviroments algorithms and architectures, theoretical foundations, and representations, and their application to real-world problems. We also welcome timely surveys and review papers.

Topics of Interest include (but are not limited to):

• Computational intelligence methodologies and implementation for changing environments
• Transfer learning
• Multitask learning
• Domain adaption
• Incremental Learning architectures
• Incremental Unsupervised and semi-supervised learning architectures
• Incremental Incremental Representation learning and disentangling
• Incremental Knowledge augmentation
• Incremental Adaptive Neuro-fuzzy systems
• Incremental and single-pass data mining
• Incremental Neural Clustering
• Incremental Neural regression
• Incremental Adaptive decision systems
• Incremental Feature selection and reduction
• Incremental Constructive Learning
• Novelty detection in Incremental learning

 Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via

https://www.easychair.org/conferences/?conf=ieeecimcdbil2015.

Important Dates (for August 2015 Issue)

15th November, 2014: Submission of Manuscripts
15th January, 2015: Notification of Review Results
15th Faburary, 2015: Submission of Revised Manuscripts
15th March, 2015: Submission of Final Manuscripts
August 2015: Publication

Guest Editors

Professor Amir Hussain,
University of Stirling,
Stirling FK9 4LA SCOTLAND, UK
Email: ahu@cs.stir.ac.uk

Professor Dacheng Tao,
University of Technology, Sydney
235 Jones Street, Ultimo, NSW 2007, Australia
Email: dacheng.tao@uts.edu.au

Professor Jonathan Wu
University of Windsor
401 Sunset Avenue, Windsor, ON, Canada
Email: jwu@uwindsor.ca

Professor Dongbin Zhao
Institute of Automation, Chinese Academy of Sciences,
No. 95, Zhongguancun East Road, Beijing 100190, China
E-mail: dongbin.zhao@gmail.com

IEEE Computational Intelligence Magazine Volume 9 Issue 2 May 2014

1. What Is Your Main IEEE Society? [Editor's Remarks]
Author(s): Ishibuchi, H.

2. President's Greeting [President's Message]
Author(s): Yao, X.

3. CIS Society Officers

4. Newly Elected CIS Administrative Committee Members (2014-2016) [Society Briefs]
Author(s): Yao, X.

5. IEEE Fellows - Class of 2014 [Society Briefs]
Author(s): Bezdek, J.

6. A Report on the CIS Second Video Competition [Society Briefs]
Author(s): Matthews, S. ; Abdool, A. ; Eliades, D. ; Coyle, D. ; Posada, J. ; Martin, E. ; Sperduti, A. ; Alippi, C. ; Estevez, P.

7. CIS Publication Spotlight
Author(s): Liu, D. ; Lin, C. ; Greenwood, G. ; Lucas, S. ; Zhang, Z.

8. Special Issue on Computational Intelligence for Community-Centric Systems [Guest Editorial]
Author(s): Kubota, N. ; Liu, H.

9. Context-Aware Personal Information Retrieval From Multiple Social Networks
Author(s): Han, X. ; Wei, W. ; Miao, C. ; Mei, J. ; Song, H.

10. Landmark-Based Methods for Temporal Alignment of Human Motions
Author(s): de Dios, P. ; Chung, P. ; Meng, Q.

11. Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics
Author(s): Li, Z. ; Hayashibe, M. ; Fattal, C. ; Guiraud, D.

12. Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
Author(s): Cambria, E. ; White, B.

13. A Memetic Algorithm for Resource Allocation Problem Based on Node-Weighted Graphs [Application Notes]
Author(s): Wu, J. ; Chang, Z. ; Yuan, L. ; Hou, Y. ; Gong, M.

14. Conference Calendar
Author(s): Haddow, P.

15. Call for Papers for Journal Special Issues

16. CEC 2015