References
- Evans DB, Hsu J, Boerma T. Universal health coverage and universal access. Bull World Health Organ. 2013;91:546–546A.
- Burkholder DB, Britton JW, Rajasekaran V, et al. Routine vs extended outpatient EEG for the detection of interictal epileptiform discharges. Neurology. 2016;86:1524–1530.
- Tatum IVWO, Winters L, Gieron M, et al. Outpatient seizure identification: results of 502 patients using computer-assisted ambulatory EEG. J Clin Neurophysiol. 2001;18:14–19.
- Castellaro C, Favaro G, Salemi G, et al. Hardware for seizure prediction: towards wearable devices to support epileptic people. In: Engineering in Medicine and Biology Society, EMBC 2011 Annual International Conference of the IEEE. Boston (MA): IEEE; 2011.
- Iasemidis LD, Shiau DS, Pardalos PM, et al. Long-term prospective on-line real-time seizure prediction. Clin Neurophysiol. 2005;116:532–544.
- Brown L, van de Molengraft J, Yazicioglu RF, et al. A low-power, wireless, 8-channel EEG monitoring headset. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. Buenos Aires, Argentina: IEEE; 2010.
- Seneviratne U, Mohamed A, Cook M, et al. The utility of ambulatory electroencephalography in routine clinical practice: a critical review. Epilepsy Res. 2013;105:1–12.
- Faulkner HJ, Arima H, Mohamed A. The utility of prolonged outpatient ambulatory EEG. Seizure. 2012;21:491–495.
- Poh MZ, Loddenkemper T, Reinsberger C, et al. Convulsive seizure detection using a wrist‐worn electrodermal activity and accelerometry biosensor. Epilepsia. 2012;53:e93–e97.
- Jeppesen J, Beniczky S, Johansen P, et al. Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot. Seizure. 2015;24:1–7.
- Nagai Y, Goldstein LH, Fenwick PBC, et al. Clinical efficacy of galvanic skin response biofeedback training in reducing seizures in adult epilepsy: a preliminary randomized controlled study. Epilepsy Behav. 2004;5:216–223.
- Chi YM, Wang Y, Wang Y-T, et al. A practical mobile dry EEG system for human computer interfaces. In: International Conference on Augmented Cognition. Berlin, Heidelberg: Springer; 2013.
- Advanced Brain Monitoring, Neurotechnology & Wireless EEG Headsets; [Internet]; [cited 2016 Aug 19]. Available from: http://www.advancedbrainmonitoring.com/neurotechnology/
- Mobita wireless EEG system, MOBITA-W-32EEG; [Internet]; [cited 2016 Aug 19]. Available from: https://www.biopac.com/product/mobita-32-channel-wireless-eeg-system/
- NATUS Medical Incorporation, Xltek LTM Systems; [Internet]; [cited August 19, 2016]. Available from: http://www.natus.com/index.cfm?page=products_1&crid=224
- Schulze-Bonhage A, Sales F, Wagner K, et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 2010;18:388–396.
- Casson AJ, Smith S, Duncan JS, et al. Wearable EEG: what is it, why is it needed and what does it entail? In: Engineering in Medicine and Biology Society (EMBS) 2008, 30th Annual International Conference of the IEEE. Vancouver, Canada: IEEE; 2008.
- Brenner RP, Drislane FW, Ebersole JS, et al. Guideline twelve: guidelines for long-term monitoring for epilepsy. J Clin Neurophysiol. 2008;25:170–180.
- Liang S-F, Shaw F-Z, Young C-P, et al. A closed-loop brain computer interface for real-time seizure detection and control. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. Buenos Aires, Argentina: IEEE; 2010.
- Raghunathan S, Gupta SK, Ward MP, et al. The design and hardware implementation of a low-power real-time seizure detection algorithm. J Neural Eng. 2009;6:056005.
- Young C-P, Hsieh C-H, Wang H-C. A low-cost real-time closed-loop epileptic seizure monitor and controller. In: Instrumentation and Measurement Technology Conference, 2009 (I2MTC'09), IEEE. Singapura, Republic of Singapura: IEEE; 2009.
- Verma N, Shoeb A, Bohorquez J, et al. A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J Solid-State Circuits. 2010;45:804–816.
- Chandler D, Bisasky J, Stanislaus JLVM, et al. Real-time multi-channel seizure detection and analysis hardware. In: Biomedical Circuits and Systems Conference (BioCAS), 2011 IEEE. San Diego (CA): IEEE; 2011.
- Van Helleputte N, Tomasik J, Galjan W, et al. A flexible system-on-chip (SoC) for biomedical signal acquisition and processing. Sens Actuators A: Phys. 2008;142:361–368.
- Hsu C-M, Liao W-Y, Luo C-H, et al. The 2.4 GHz biotelemetry chip for healthcare monitoring system. Sens Actuators A: Phys. 2007;139:245–251.
- Boquete L, Ascariz JMR, Cantos J, et al. A portable wireless biometric multi-channel system. Measurement. 2012;45:1587–1598.
- Myung BR, Yoo SK. Development of 16-channels compact EEG system using real-time high-speed wireless transmission. Engineering. 2013;5:93.
- Lin C-T, Chang C-J, Lin B-S, et al. A real-time wireless brain–computer interface system for drowsiness detection. IEEE Trans Biomed Circuits Syst. 2010;4:214–222.
- Yuce MR. Implementation of wireless body area networks for healthcare systems. Sens Actuators A: Phys. 2010;162:116–129.
- Voros NS, Antonopoulos CP. Cyberphysical systems for epilepsy and related brain disorders: multi-parametric monitoring and analysis for diagnosis and optimal disease management. London: Springer; 2015.
- Cognionics Incorporation, 72-Channel Dry EEG Headset System; [Internet]; [cited 2016 Aug 23]. Available from: http://www.cognionics.com/index.php/products/hd-eeg-systems/72-channel-system
- Brain Products GmbH, Products & Applications, MOVE; [Internet]; [cited 2016 Aug 19]. Available from: http://www.brainproducts.com/productdetails.php?id=40
- Duan J, Chen C, Pun SH, et al. A wearable wireless general purpose bio-signal acquisition prototype system for home healthcare. In: 2012 International Conference on Biomedical Engineering and Biotechnology (iCBEB). Macau: IEEE; 2012.
- Liao L-D, Chen C-Y, Wang IJ, et al. Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil. 2012;9:5.
- Maia C, Nogueira LM, Pinho LM. Evaluating android OS for embedded real-time systems. In: 6th International Workshop on Operating Systems Platforms for Embedded Real-Time Applications (OSPERT); 2010.
- Christini DJ, Stein KM, Markowitz SM, et al. Practical real-time computing system for biomedical experiment interface. Ann Biomed Eng. 1999;27:180–186.
- Hsu C-W, Chang C-C, Lin C-J. A practical guide to support vector classification. Taipei, Taiwan: Department of Computer Science, National Taiwan University; 2010.
- Brown JH, Martin B. How fast is fast enough? Choosing between Xenomai and Linux for real-time applications. In: Real-Time and Embedded Technology and Applications Symposium, 2002. Proceedings Eighth IEEE. San Jose (CA): IEEE; 2002.
- Abeni L, Goel A, Krasic C, et al. A measurement-based analysis of the real-time performance of Linux. In: 2012 International Conference on Biomedical Engineering (ICoBE). Penang, Malaysia: IEEE; 2012.
- Alam M, Azad A. Development of biomedical data acquisition system in Hard Real-Time Linux environment. In: 2012 International Conference on Biomedical Engineering (ICoBE). Penang, Malaysia: IEEE; 2012.
- Love R. Linux kernel development. Crawfordsville: Pearson Education; 2010.
- Pinho F, Correia JH, Sousa NJ, et al. Wireless and wearable EEG acquisition platform for ambulatory monitoring. In: 2014 IEEE 3rd International Conference on Serious Games and Applications for Health (SeGAH). Rio de Janeiro, Brazil: IEEE; 2014.
- Homan RW, Herman J, Purdy P. Cerebral location of international 10–20 system electrode placement. Electroencephalogr Clin Neurophysiol. 1987;66:376–382.
- Brain Products GmbH, Products & Applications, actiCAP; [Internet]; [cited 2016 Oct 19]. Available from: http://www.brainproducts.com/productdetails.php?id=4
- Corporation M-M. 0908-Spring-loaded Pin 2017; [Internet]; [cited 2017 Jun 26]. Available from: https://www.mill-max.com/products/pin/0908
- Instruments T. Low-noise, 8-channel, 24-bit analog front-end for biopotential measurements ADS1299. Datasheet; 2012.
- IEC 60601-1. Medical electrical equipment – general requirements for basic safety and essential performance; 2015.
- Pinho F, Ferreira J, Reis J, et al. Epileptic event detection algorithm for ambulatory monitoring platforms. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Lisbon, Portugal: IEEE; 2014.
- Adeli H, Ghosh-Dastidar S. Automated EEG-based diagnosis of neurological disorders: inventing the future of neurology. Boca Raton, FL: CRC Press; 2010.
- Safieddine D, Kachenoura A, Albera L, et al. Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches. EURASIP J Adv Signal Process. 2012;2012:1–15.
- Moré JJ. The Levenberg–Marquardt algorithm: implementation and theory. Numerical analysis. Berlin: Springer; 1978. p. 105–116.
- Casal L, La Mura G. Skin-electrode impedance measurement during ECG acquisition: method’s validation. J Phys.: Conf Ser. 2016;705(1).
- Team FE. Embedded Linux experts – free electrons; [Internet]; [cited 2017 Jun 26]. Available from: http://free-electrons.com/
- Lopez-Gordo M, Sanchez-Morillo D, Valle FP. Dry EEG electrodes. Sensors (Basel). 2014;14:12847–12870.
- iMac external features, ports, and connectors; [Internet]; [cited 2017 Jan 23]. Available from: https://support.apple.com/en-us/HT204392
- Goldberger AL, Amaral LA, Glass L, et al. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation. 2000;101:e215–ee20.
- Brain Products GmbH, Products & Applications, actiCHamp; [Internet]; [cited 2016 Oct 19]. Available from: http://www.brainproducts.com/productdetails.php?id=42
- Saboor A, Rezeika A, Stawicki P, et al. SSVEP-based BCI in a smart home scenario. In: International Work-Conference on Artificial Neural Networks. Cádiz, Spain: Springer; 2017.
- Pierce AM, Crouse MD, Green JJ. Evidence for an attentional component of inhibition of return in visual search. Psychophysiology. 2017 [Jun 5]. doi: 10.1111/psyp.12905.
- Lourenço PR, Abbott W, Faisal AA. Supervised EEG ocular artefact correction through eye-tracking. Advances in neurotechnology, electronics and informatics. Rome: Springer; 2016. p. 99–113.
- Tautan A-M, Mihajlovic V, Chen Y-H, et al. Signal quality in dry electrode EEG and the relation to skin-electrode contact impedance magnitude. In: BIODEVICES. Lorre Valey, France; 2014.
- Tăuţan A-M, Serdijn W, Mihajlović V, et al. Framework for evaluating EEG signal quality of dry electrode recordings. In: 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE; 2013.
- Acunzo DJ, MacKenzie G, van Rossum MC. Systematic biases in early ERP and ERF components as a result of high-pass filtering. J Neurosci Methods. 2012;209:212–218.
- Ries AJ, Touryan J, Vettel J, et al. A comparison of electroencephalography signals acquired from conventional and mobile systems. J Neurosci Neuroeng. 2014;3:10–20.
- Zander TO, Lehne M, Ihme K, et al. A dry EEG-system for scientific research and brain–computer interfaces. Front Neurosci. 2011;5:53.
- Hajra SG, Liu CC, Song X, et al. Developing brain vital signs: initial framework for monitoring brain function changes over time. Front Neurosci. 2016;10.
- Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002;49:1181–1186.
- Renard Y, Lotte F, Gibert G, et al. Openvibe: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments. Presence. 2010;19:35–53.
- Falzon O, Camilleri KP, Muscat J. The analytic common spatial patterns method for EEG-based BCI data. J Neural Eng. 2012;9:045009.
- Kothe CA, Makeig S. BCILAB: a platform for brain–computer interface development. J Neural Eng. 2013;10:056014.
- Legény J, Viciana-Abad R, Lécuyer A. Toward contextual SSVEP-based BCI controller: smart activation of stimuli and control weighting. IEEE Trans Comput Intell AI Games. 2013;5:111–116.
- Yu Y-H, Lu S-W, Chuang C-H, et al. An inflatable and wearable wireless system for making 32-channel electroencephalogram measurements. IEEE Trans Neural Syst Rehabil Engng 2016;24:806–813.
- Matthews R, McDonald NJ, Fridman I, et al. The invisible electrode – zero prep time, ultra low capacitive sensing. In: Proceedings of the 11th International Conference on Human-Computer Interaction, Las Vegas, USA; 2005.
- Toyama S, Takano K, Kansaku K. A non-adhesive solid-gel electrode for a non-invasive brain–machine interface. Front Neur. 2012;3.
- Lee SM, Kim JH, Byeon HJ, et al. A capacitive, biocompatible and adhesive electrode for long-term and cap-free monitoring of EEG signals. J Neural Eng. 2013;10:036006.
- Wyckoff SN, Sherlin LH, Ford NL, et al. Validation of a wireless dry electrode system for electroencephalography. J Neuroeng Rehabil. 2015;12:1.
- Ferree TC, Luu P, Russell GS, et al. Scalp electrode impedance, infection risk, and EEG data quality. Clin Neurophysiol. 2001;112:536–544.
- Wu J, Jia W, Xu C, et al. Impedance analysis of ZnO nanowire coated dry EEG electrodes. JBEI. 2017;3:44.
- Stern JM, Engel J. Atlas of EEG patterns. 1st ed. Philadelphia (PA): Lippincott Williams & Wilkins; 2005.
- Lin C-T, Liao L-D, Liu Y-H, et al. Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Trans Biomed Eng. 2011;58:1200–1207.
- Chi YM, Wang Y-T, Wang Y, et al. Dry and noncontact EEG sensors for mobile brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2012;20:228–235.
- Salvo P, Raedt R, Carrette E, et al. A 3D printed dry electrode for ECG/EEG recording. Sens Actuators A: Phys. 2012;174:96–102.
- Zhou W, Song R, Pan X, et al. Fabrication and impedance measurement of novel metal dry bioelectrode. Sens Actuators A: Phys. 2013;201:127–133.
- Fiedler P, Griebel S, Pedrosa P, et al. Multichannel EEG with novel Ti/TiN dry electrodes. Sens Actuators A: Phys. 2015;221:139–147.
- G. Tec, g.Nautilus – g.tec’s wireless EEG system with active electrodes; [Internet]; [cited 2016 Aug 19]. Available from: http://www.gtec.at/Products/Hardware-and-Accessories/g.Nautilus-Specs-Features
- Liu Z-h. OMAPL138-based systematic study on portable EEG detection system. Int J Sci Res. 2016;5:3.
- Wali MK, Murugappan M, Ahmmad RB. Development of EEG data acquisition device by using single board computer. IJMEI. 2013;5:191–200.
- Hazrati MK, Husin HM, Hofmann UG. Wireless brain signal recordings based on capacitive electrodes. In: 2013 IEEE 8th International Symposium on Intelligent Signal Processing (WISP). Funchal, Portugal: IEEE; 2013.
- Wang Y, Gao X, Hong B, et al. Brain–computer interfaces based on visual evoked potentials. IEEE Eng Med Biol Mag. 2008;27:64–71.
- Lin Y-P, Wang Y, Jung T-P. Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset. J Neuroeng Rehabil. 2014;11:1.
- Martišius I, Damaševičius R. A prototype SSVEP based real time BCI gaming system. Comput Intell Neurosci. 2016;2016:Article ID 3861425. doi: 10.1155/2016/3861425
- Guo L, Rivero D, Dorado J, et al. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J Neurosci Methods. 2010;191:101–109.
- Zainuddin Z, Huong LK, Pauline O. Reliable epileptic seizure detection using an improved wavelet neural network. AMJ. 2013;6:308.
- Yadav R, Agarwal R, Swamy MNS. A novel morphology-based classifier for automatic detection of epileptic seizures. In: 2010 IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). Buenos Aires, Argentina: IEEE; 2010.
- Tzallas AT, Tsipouras MG, Tsalikakis DG, et al. Automated epileptic seizure detection methods: a review study. In: Stevanovic D, editor. Epilepsy - Histological, Electroencephalographic and Psychological Aspects, InTech, DOI: 10.5772/31597. Available from: https://www.intechopen.com/books/epilepsy-histological-electroencephalographic-and-psychological-aspects/automated-epileptic-seizure-detection-methods-a-review-study
- Urigüen JA, Garcia-Zapirain B. EEG artifact removal-state-of-the-art and guidelines. J Neural Eng. 2015;12:031001.
- Vergult A, De Clercq W, Palmini A, et al. Improving the interpretation of Ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia. 2007;48:950–958.
- Gwin JT, Gramann K, Makeig S, et al. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010;103:3526–3534.
- Myers MH, Padmanabha A, Hossain G, et al. Seizure prediction and detection via phase and amplitude lock values. Front Hum Neurosci. 2016;10.