Age-related differences in SSVEP-based BCI performance

BrainComputer Interface (BCI) systems analyze brain signals to generate control commands for computer applications or external devices. Utilized as alternative communication channel, BCIs have the potential to assist people with severe motor disabilities to interact with their environment and to participate in daily life activities. Handicapped people from all age groups could benefit from such BCI technologies. Although some papers have previously reported slightly worse BCI performance by older subjects, in many studies BCI systems were tested with young subjects only.In the presented paper age-associated differences in BCI performance were investigated. We compared accuracy and speed of a steady-state visual evoked potential (SSVEP)-based BCI spelling application controlled by participants of two different equally sized age groups. Twenty subjects (eleven female and nine male) participated in this study; each age group consisted of ten subjects, ranging from 19 to 27 years and from 64 to 76 years. Our results confirm that elderly people may have a deteriorated information transfer rate (ITR). The mean (SD) ITR of the young age group was 27.36 (6.50) bit/min while the elderly people achieved a significantly lower ITR of 16.10 (5.90) bit/min. The average time window length associated with the signal classification was usually larger for the participants of advanced age. These findings show that the subject age must be taken into account during the development of SSVEP-based applications.

[1]  Peter Desain,et al.  Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing , 2015, PloS one.

[2]  Ivan Volosyak,et al.  Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[3]  Ivan Volosyak,et al.  STEADY-STATE VISUAL EVOKED POTENTIAL RESPONSE - IMPACT OF THE TIME SEGMENT LENGTH , 2010 .

[4]  Arne Robben,et al.  Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface , 2012, Neurocomputing.

[5]  Ivan Volosyak,et al.  SSVEP-based Bremen–BCI interface—boosting information transfer rates , 2011, Journal of neural engineering.

[6]  K. Müller,et al.  Effect of higher frequency on the classification of steady-state visual evoked potentials , 2016, Journal of neural engineering.

[7]  Jonathan R Wolpaw,et al.  A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[8]  A. Kübler,et al.  Face stimuli effectively prevent brain–computer interface inefficiency in patients with neurodegenerative disease , 2013, Clinical Neurophysiology.

[9]  Chun-Yen Chang,et al.  Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  T. Vaughan,et al.  Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users. , 2015, Archives of physical medicine and rehabilitation.

[11]  Ivan Volosyak,et al.  A Dictionary-Driven SSVEP Speller with a Modified Graphical User Interface , 2011, IWANN.

[12]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[13]  Paul McCullagh,et al.  Ethical Challenges Associated with the Development and Deployment of Brain Computer Interface Technology , 2013, Neuroethics.

[14]  Wolfgang Rosenstiel,et al.  Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning , 2012, PloS one.

[15]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[16]  Diana Valbuena,et al.  Age-Specific Mechanisms in an SSVEP-Based BCI Scenario: Evidences from Spontaneous Rhythms and Neuronal Oscillators , 2012, Comput. Intell. Neurosci..

[17]  N.S. Dias,et al.  Subject Age in P300 BCI , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[18]  A Graser,et al.  BCI Demographics II: How Many (and What Kinds of) People Can Use a High-Frequency SSVEP BCI? , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Piotr Stawicki,et al.  Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard , 2015, Front. Neurosci..

[20]  Ivan Volosyak,et al.  Evaluation of different spelling layouts for SSVEP based BCIs , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Brian R. Tietz,et al.  Deciding Which Way to Go: How Do Insects Alter Movements to Negotiate Barriers? , 2012, Front. Neurosci..

[22]  Feng Wan,et al.  Adaptive time-window length based on online performance measurement in SSVEP-based BCIs , 2015, Neurocomputing.

[23]  Emmanuel K. Kalunga,et al.  Online SSVEP-based BCI using Riemannian geometry , 2015, Neurocomputing.

[24]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[25]  Luca Mainardi,et al.  Performance measurement for brain–computer or brain–machine interfaces: a tutorial , 2014, Journal of neural engineering.

[26]  Brendan Z. Allison,et al.  How Many People Could Use an SSVEP BCI? , 2012, Front. Neurosci..

[27]  B. Allison,et al.  BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Yijun Wang,et al.  Visual and Auditory Brain–Computer Interfaces , 2014, IEEE Transactions on Biomedical Engineering.

[29]  Paolo Bonato,et al.  Patient specific ankle-foot orthoses using rapid prototyping , 2011, Journal of NeuroEngineering and Rehabilitation.

[30]  Andrew Pipingas,et al.  A steady state visually evoked potential investigation of memory and ageing , 2009, Brain and Cognition.

[31]  A. Kübler,et al.  A Brain–Computer Interface Controlled Auditory Event‐Related Potential (P300) Spelling System for Locked‐In Patients , 2009, Annals of the New York Academy of Sciences.

[32]  Pablo F. Diez,et al.  Asynchronous BCI control using high-frequency SSVEP , 2011, Journal of NeuroEngineering and Rehabilitation.

[33]  Piotr Stawicki,et al.  A User-Friendly Dictionary-Supported SSVEP-based BCI Application , 2016, Symbiotic.

[34]  Bernhard Schölkopf,et al.  A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs) , 2013 .