Araştırma Makalesi
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Beyin Bilgisayar Arayüzü ve Geleceği

Yıl 2022, Cilt: 5 Sayı: 2, 135 - 141, 31.12.2022

Öz

İnsan beyninin işleyişi henüz tam olarak keşfedilmemiş esrarengiz yapısı ile araştırmacıların odağında olan bir organdır. Günümüzde tıptan başka mühendislikten eğitime, spordan finansa kadar birçok disiplinde yapılan araştırmalarda dikkate alınmaktadır. Özellikle kamuoyunda “düşünce gücü” olarak adlandırılan literatürdeki ismi ile beyin bilgisayar arayüzleri geleceğin teknolojileri arasında gösterilmektedir. Bu teknoloji insanların sadece düşünceleri ile bilgisayar gibi elektronik cihazları kontrol edebilmelerini olanaklı hale getiren sistemlerdir. Öyle ki, insanlar düşünceleri ile yazı yazabilmekte, bir nöroprotezi hareket ettirebilmektedir. Bu çalışmada bu teknolojinin veri kaydetme yaklaşımları, dünya literatüründe üretilen araştırma sayılarının analizi ile gelecekteki çalışma sahaları hakkında sonuçlar sunulmuştur.

Kaynakça

  • Guo, X., Shen, Z., Zhang, Y., & Wu, T. (2019). Review on the application of artificial intelligence in smart homes. Smart Cities, 2(3), 402-420.
  • Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.
  • Pandian, A. P. (2019). Artificial intelligence application in smart warehousing environment for automated logistics. Journal of Artificial Intelligence, 1(02), 63-72.
  • Aydemir, Ö. (2008). Beyin bilgisayar arayüzü uygulamalarına yönelik EEG işaretleri için öznitelik çıkarma Yüksek lisans tezi, Karadeniz Teknik Üniversitesi/Fen Bilimleri Enstitüsü.
  • Siuly, S., & Li, Y. (2012). Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 526-538.
  • Kaur, A. (2021). Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review. Journal of Medical Engineering & Technology, 45(1), 61-74.
  • Li, H., Ding, M., Zhang, R., & Xiu, C. (2022). Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomedical Signal Processing and Control, 72, 103342.
  • Pérez-Reynoso, F. D., Rodríguez-Guerrero, L., Salgado-Ramírez, J. C., & Ortega-Palacios, R. (2021). Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. Sensors, 21(17), 5882.
  • Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalography and clinical neurophysiology, 78(3), 252-259.
  • Marshall, D., Coyle, D., Wilson, S., & Callaghan, M. (2013). Games, gameplay, and BCI: the state of the art. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 82-99.
  • Zabcikova, M., Koudelkova, Z., Jasek, R., & Lorenzo Navarro, J. J. (2022). Recent advances and current trends in brain‐computer interface research and their applications. International Journal of Developmental Neuroscience, 82(2), 107-123.
  • Kosmyna, N., Tarpin-Bernard, F., Bonnefond, N., & Rivet, B. (2016). Feasibility of BCI control in a realistic smart home environment. Frontiers in human neuroscience, 10, 416.
  • Aydemir, Ö., & Kayıkçıoğlu, T. (2009). EEG tabanlı beyin bilgisayar arayüzleri. Akademik Bilişim, 9, 11-13.
  • Ahn, M., & Jun, S. C. (2015). Performance variation in motor imagery brain–computer interface: a brief review. Journal of neuroscience methods, 243, 103-110.
  • Zhang, J., & Wang, M. (2021). A survey on robots controlled by motor imagery brain-computer interfaces. Cognitive Robotics, 1, 12-24.
  • Quiles, E., Suay, F., Candela, G., Chio, N., Jiménez, M., & Álvarez-Kurogi, L. (2020). Low-cost robotic guide based on a motor imagery brain–computer interface for arm assisted rehabilitation. International journal of environmental research and public health, 17(3), 699.
  • Leeuwis, N., Paas, A., & Alimardani, M. (2021). Vividness of visual imagery and personality impact motor-imagery brain computer interfaces. Frontiers in Human Neuroscience, 15, 634748.
  • Loizidou, P., Rios, E., Marttini, A., Keluo-Udeke, O., Soetedjo, J., Belay, J., ... & Speier, W. (2022). Extending brain-computer interface access with a multilingual language model in the P300 speller. Brain-Computer Interfaces, 9(1), 36-48.
  • Korkmaz, O. E., Aydemir, O., Oral, E. A., & Ozbek, I. Y. (2022). An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation. PloS one, 17(4), e0265904.
  • Won, K., Kwon, M., Ahn, M., & Jun, S. C. (2022). EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces. Scientific Data, 9(1), 1-11.
  • Ahn, S., Kim, K., & Jun, S. C. (2016). Steady-state somatosensory evoked potential for brain-computer interface—present and future. Frontiers in human neuroscience, 716.
  • https://www.webofscience.com/wos/woscc/basic-search
Yıl 2022, Cilt: 5 Sayı: 2, 135 - 141, 31.12.2022

Öz

Kaynakça

  • Guo, X., Shen, Z., Zhang, Y., & Wu, T. (2019). Review on the application of artificial intelligence in smart homes. Smart Cities, 2(3), 402-420.
  • Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.
  • Pandian, A. P. (2019). Artificial intelligence application in smart warehousing environment for automated logistics. Journal of Artificial Intelligence, 1(02), 63-72.
  • Aydemir, Ö. (2008). Beyin bilgisayar arayüzü uygulamalarına yönelik EEG işaretleri için öznitelik çıkarma Yüksek lisans tezi, Karadeniz Teknik Üniversitesi/Fen Bilimleri Enstitüsü.
  • Siuly, S., & Li, Y. (2012). Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 526-538.
  • Kaur, A. (2021). Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review. Journal of Medical Engineering & Technology, 45(1), 61-74.
  • Li, H., Ding, M., Zhang, R., & Xiu, C. (2022). Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomedical Signal Processing and Control, 72, 103342.
  • Pérez-Reynoso, F. D., Rodríguez-Guerrero, L., Salgado-Ramírez, J. C., & Ortega-Palacios, R. (2021). Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. Sensors, 21(17), 5882.
  • Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalography and clinical neurophysiology, 78(3), 252-259.
  • Marshall, D., Coyle, D., Wilson, S., & Callaghan, M. (2013). Games, gameplay, and BCI: the state of the art. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 82-99.
  • Zabcikova, M., Koudelkova, Z., Jasek, R., & Lorenzo Navarro, J. J. (2022). Recent advances and current trends in brain‐computer interface research and their applications. International Journal of Developmental Neuroscience, 82(2), 107-123.
  • Kosmyna, N., Tarpin-Bernard, F., Bonnefond, N., & Rivet, B. (2016). Feasibility of BCI control in a realistic smart home environment. Frontiers in human neuroscience, 10, 416.
  • Aydemir, Ö., & Kayıkçıoğlu, T. (2009). EEG tabanlı beyin bilgisayar arayüzleri. Akademik Bilişim, 9, 11-13.
  • Ahn, M., & Jun, S. C. (2015). Performance variation in motor imagery brain–computer interface: a brief review. Journal of neuroscience methods, 243, 103-110.
  • Zhang, J., & Wang, M. (2021). A survey on robots controlled by motor imagery brain-computer interfaces. Cognitive Robotics, 1, 12-24.
  • Quiles, E., Suay, F., Candela, G., Chio, N., Jiménez, M., & Álvarez-Kurogi, L. (2020). Low-cost robotic guide based on a motor imagery brain–computer interface for arm assisted rehabilitation. International journal of environmental research and public health, 17(3), 699.
  • Leeuwis, N., Paas, A., & Alimardani, M. (2021). Vividness of visual imagery and personality impact motor-imagery brain computer interfaces. Frontiers in Human Neuroscience, 15, 634748.
  • Loizidou, P., Rios, E., Marttini, A., Keluo-Udeke, O., Soetedjo, J., Belay, J., ... & Speier, W. (2022). Extending brain-computer interface access with a multilingual language model in the P300 speller. Brain-Computer Interfaces, 9(1), 36-48.
  • Korkmaz, O. E., Aydemir, O., Oral, E. A., & Ozbek, I. Y. (2022). An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation. PloS one, 17(4), e0265904.
  • Won, K., Kwon, M., Ahn, M., & Jun, S. C. (2022). EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces. Scientific Data, 9(1), 1-11.
  • Ahn, S., Kim, K., & Jun, S. C. (2016). Steady-state somatosensory evoked potential for brain-computer interface—present and future. Frontiers in human neuroscience, 716.
  • https://www.webofscience.com/wos/woscc/basic-search
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri
Yazarlar

Önder Aydemir 0000-0002-1177-8518

Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 30 Kasım 2022
Kabul Tarihi 28 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 5 Sayı: 2

Kaynak Göster

APA Aydemir, Ö. (2022). Beyin Bilgisayar Arayüzü ve Geleceği. Journal of Investigations on Engineering and Technology, 5(2), 135-141.