Human Computer Interactions for Amyotrophic Lateral Sclerosis Patients

In this chapter, alternative communication and device control channels, which are helpful for Amyotrophic lateral sclerosis (ALS) patients, are introduced. In this context, human computer interactions (HCIs) will be discussed in three respects; electrical brain activities, eye movements and hemoglobin level in the blood. With technological advances, fighting or minimization side effects of the diseases is the main purpose of biomedical research. Under this motto, this chapter focuses on HCIs for individuals suffering from motor neuron diseases. ALS is a progressive neurodegenerative disease caused by the degeneration of motor neurons. ALS or other tetraplegic clinical conditions, otherwise known as the locked-in syndrome, have severe disabilities in controlling muscles and consequently have problems in moving the entire body. Some of these patients can only move their eyes. In severe conditions of the progressive motor neuron diseases, patients cannot move their eyes nor can they speak. Establishing an efficient communication channel without overt speaking and hand motions makes the patient’s life a bit easier and increases their quality of life. ALS occurs in between 4 and 8 out of every 100,000 individuals and only a small percentage of cases arise from a known genetic cause (Parker & Parker, 2007). Concerning other motor neuron diseases or speaking and muscular disabilities, there are more than 100 million potential users in need of alternative channels such as brain computer interface (BCI) for communicating with their environment or for controlling devices (Guger, 2008). Considering life span extension and increasing causes of injuries including traffic accidents and explosions, which may result in spinal cord injuries in serious cases, the need for an efficient communication or control channel has been drastically increasing. HCIs are a research field which includes interactions such as communication and device/machine control between a user and a computer. The aim of the HCI is to improve performance of the interaction, meaning a minimization of the barrier between the human and the computer. Accurate and fast interpretation of what the user wants to do as well as a correct understanding by the computer of the user's intentions or demand is the aim of this research field. Man-machine interface (MMI), brain-machine interface (BMI) and BCI can be thought of as applications of HCIs. If communication or control is established directly from the brain, it is called BCI and it is the only method of interaction for the individuals with complete

[1]  Keum-Shik Hong,et al.  Neural Networks Training Based on Sequential Extended Kalman Filtering for Single Trial EEG Classification , 2010, 2010 Second International Conference on Knowledge and Systems Engineering.

[2]  Ching-Hsing Luo,et al.  Portable clinical EOG instrument system , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[3]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled. , 2003, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  J. Mayhew,et al.  Spectroscopic Analysis of Neural Activity in Brain: Increased Oxygen Consumption Following Activation of Barrel Cortex , 2000, NeuroImage.

[7]  M Juhola,et al.  Detection of saccadic eye movements using a non-recursive adaptive digital filter. , 1985, Computer methods and programs in biomedicine.

[8]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[9]  A. Gevins,et al.  Beyond topographic mapping: Towards functional-anatomical imaging with 124-channel EEGs and 3-D MRIs , 2005, Brain Topography.

[10]  Raveendran Paramesran,et al.  Enhanced ${\mu }$ Rhythm Extraction Using Blind Source Separation and Wavelet Transform , 2009, IEEE Transactions on Biomedical Engineering.

[11]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[12]  Steven Lemm,et al.  BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[13]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[14]  W. M. Bukhari,et al.  A wavelet approach on energy distribution of eye movement potential towards direction , 2010, 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA).

[15]  Yoshinobu Ebisawa,et al.  Development of head-mounted display with eye-gaze detection function for the severely disabled , 2008, 2008 IEEE Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[16]  R.S. Allison,et al.  Combined head and eye tracking system for dynamic testing of the vestibular system , 1996, IEEE Transactions on Biomedical Engineering.

[17]  S. Bunce,et al.  Functional Brain Imaging Using Near-Infrared Technology Assessing Cognitive Activity in Real-Life Situations , 2007 .

[18]  Yoshiki Uchikawa,et al.  Development of eye pointer with free head-motion [EOG-based system] , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[19]  Chung-Min Wu,et al.  EOG single switch morse code translate input device for individuals with the motor neuron disease , 2007, TENCON 2007 - 2007 IEEE Region 10 Conference.

[20]  G. Pfurtscheller,et al.  Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Siew-Cheok Ng,et al.  Enhanced ${\mu }$ Rhythm Extraction Using Blind Source Separation and Wavelet Transform , 2009, IEEE Transactions on Biomedical Engineering.

[22]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  W. J. Nowack.  Neocortical Dynamics and Human EEG Rhythms , 1995, Neurology.

[24]  Marc M. Van Hulle,et al.  Self adaptive BCI as service-oriented information system for patients with communication disabilities , 2010 .

[25]  Yaoyao Hao,et al.  A portable wireless eye movement-controlled Human-Computer Interface for the disabled , 2009, 2009 ICME International Conference on Complex Medical Engineering.

[26]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Rui Wang,et al.  Feature Extraction and Classification of Mental EEG for Motor Imagery , 2009, ICNC.

[28]  Stefan Carmien,et al.  Affective brain-computer interfaces: Psychophysiological markers of emotion in healthy persons and in persons with amyotrophic lateral sclerosis , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[29]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[30]  V. A. Konyshev,et al.  A P300-based brain—computer interface , 2007, Meditsinskaia tekhnika.

[31]  Worthy N. Martin,et al.  Human-computer interaction using eye-gaze input , 1989, IEEE Trans. Syst. Man Cybern..

[32]  A. Buttfield,et al.  Towards a robust BCI: error potentials and online learning , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  A.R. Kherlopian,et al.  Electrooculogram based system for computer control using a multiple feature classification model , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  B. Chance,et al.  A novel method for fast imaging of brain function, non-invasively, with light. , 1998, Optics express.

[35]  Katsumi Takahashi,et al.  Human interface using PC display with head pointing device for eating assist robot and emotional evaluation by GSR sensor , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[36]  Ali Bülent Usakli,et al.  Modeling of movement-related potentials using a fractal approach , 2010, Journal of Computational Neuroscience.

[37]  Rajesh Singla,et al.  EOG and EMG based virtual keyboard: A brain-computer interface , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[38]  A. Terry Bahill,et al.  Predicting Final Eye Position Halfway Through a Saccade , 1983, IEEE Transactions on Biomedical Engineering.

[39]  Nevzat G. Gencer,et al.  USB‐Based 256‐Channel Electroencephalographic Data Acquisition System for Electrical Source Imaging of the Human Brain , 2007 .

[40]  M. Miyashita,et al.  Mouse cursor control system using electrooculogram signals , 2010, 2010 World Automation Congress.

[41]  Erman Acar,et al.  “Prototype hardware design for Brain Computer Interface applications” , 2009, 2009 14th National Biomedical Engineering Meeting.

[42]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[43]  D.J. McFarland,et al.  Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[44]  Adi Indrayanto,et al.  A computer cursor controlled by eye movements and voluntary eye winks using a single channel EOG , 2009, 2009 International Conference on Electrical Engineering and Informatics.

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

[46]  G. Norris,et al.  The Eye Mouse, an eye communication device , 1997, Proceedings of the IEEE 23rd Northeast Bioengineering Conference.

[47]  Ricardo Chavarriaga,et al.  Adaptation of hybrid human-computer interaction systems using EEG error-related potentials , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[48]  S. Bunce,et al.  Functional near-infrared spectroscopy , 2006, IEEE Engineering in Medicine and Biology Magazine.

[49]  J. R. Wolpaw.  Brain-computer interfaces: signals, methods, and goals , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[50]  S. Bunce,et al.  Detecting cognitive activity related hemodynamic signal for brain computer interface using functional near infrared spectroscopy , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[51]  Wang Rui,et al.  Feature Extraction and Classification of Mental EEG for Motor Imagery , 2009, 2009 Fifth International Conference on Natural Computation.

[52]  Chao Li,et al.  Realization of stress detection using psychophysiological signals for improvement of human-computer interactions , 2005, Proceedings. IEEE SoutheastCon, 2005..

[53]  Serkan Gurkan,et al.  Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard , 2010, IEEE Transactions on Instrumentation and Measurement.

[54]  S. Honda,et al.  Electro-oculography mouse for amyotrophic lateral sclerosis patients , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[55]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

[56]  G. Pfurtscheller,et al.  Continuous EEG classification during motor imagery-simulation of an asynchronous BCI , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[57]  Yoshiki Uchikawa,et al.  Development 01 eye pointer with free head-motion , 1998 .

[58]  Stephen J. Roberts,et al.  Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation , 2004, IEEE Transactions on Biomedical Engineering.

[59]  Gert Pfurtscheller,et al.  Automatic differentiation of multichannel EEG signals , 2001, IEEE Transactions on Biomedical Engineering.

[60]  Chung-Hsien Kuo,et al.  Eyeglasses based electrooculography human-wheelchair interface , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[61]  E. Donchin,et al.  Brain-computer interface research at the university of south Florida cognitive psychophysiology laboratory: the P300 speller , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[62]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[63]  Henrik Ohlsson,et al.  A Brain Computer Interface for Communication Using Real-Time fMRI , 2010, 2010 20th International Conference on Pattern Recognition.

[64]  G. Vecchiato,et al.  A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[65]  Khalil Arshak,et al.  Towards a brain controlled assistive technology for powered mobility , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[66]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[67]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[68]  Laura Astolfi,et al.  Processing of Brain Signals by Using Hemodynamic and Neuroelectromagnetic Modalities , 2010, Comput. Intell. Neurosci..

[69]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[70]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[71]  Eilon Vaadia,et al.  Motor Cortex in Voluntary Movements: A Distributed System for Distributed Functions , 2007 .

[72]  A. Grinvald,et al.  Vascular imprints of neuronal activity: relationships between the dynamics of cortical blood flow, oxygenation, and volume changes following sensory stimulation. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[73]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[74]  Nevzat G. Gencer,et al.  Performance tests of a novel electroencephalographic data-acquisition system , 2007 .

[75]  T. Sugi,et al.  Construction of simple communication method by use of neuro-biological signals , 2009, 2009 ICCAS-SICE.

[76]  Michio Inoue,et al.  Application of event related brain potentials to communication aids , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[77]  Chun-Liang Hsu,et al.  EOG-based signal detection and verification for HCI , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[78]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[79]  Paolo Bonato,et al.  Characterization of motor unit behavior in patients with amyotrophic lateral sclerosis , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[80]  Batu Akan,et al.  A Human-Computer Interface (HCI) based on Electrooculogram (EOG) for Handicapped , 2007, 2007 IEEE 15th Signal Processing and Communications Applications.

[81]  Gihan Shin,et al.  Vision-Based Multimodal Human Computer Interface Based on Parallel Tracking of Eye and Hand Motion , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[82]  Marc Van Hulle,et al.  Self adaptive BCI as service-oriented information system for patients with communication disabilities , 2010, 4th International Conference on New Trends in Information Science and Service Science.

[83]  T. N. Lal,et al.  Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.