{ "id": 479086, "title": "HMS: Harmful Brain Activity Classification : Relevant Papers for this competition", "url": "/competitions/hms-harmful-brain-activity-classification/discussion/479086", "author_name": "C R Suthikshn Kumar", "post_date": "2024-02-23T04:25:27.892000", "votes": -6, "comment_count": 0, "views": 0, "content": "

Refer to \"HMS: Harmful Brain Activity Classification\" Competition.
\nhttps://www.kaggle.com/competitions/hms-harmful-brain-activity-classification

\n

I am sharing here a list of useful and relevant papers for this competition:

\n
    \n
  1. . Fahmi, V. Aprianti, B. Siregar and M. Aziz, \"Design of Classification of Human Stress Levels Based on Brain Wave Observation Using EEG with K-NN Algorithm,\" 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), Medan, Indonesia, 2022, pp. 200-205, doi: 10.1109/ELTICOM57747.2022.10038004. Web: https://ieeexplore.ieee.org/document/10038004
    2. \n
  2. Caiola M, Babu A, Ye M (2023) EEG classification of traumatic brain injury and stroke from a nonspecific population using neural networks. PLOS Digit Health 2(7): e0000282. https://doi.org/10.1371/journal.pdig.0000282 Web: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000282
    3. \n
  3. Samee NA, Mahmoud NF, Atteia G, Abdallah HA, Alabdulhafith M, Al-Gaashani MSAM, Ahmad S, Muthanna MSA. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel). 2022 Oct 20;12(10):2541. doi: 10.3390/diagnostics12102541. PMID: 36292230; PMCID: PMC9600529. Web: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600529/
    4. \n
  4. Altan, Gokhan & Kutlu, Yakup & Allahverdi, Novruz. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers. 205-205. 10.18100/ijamec.270307. Web: https://www.researchgate.net/publication/311850531_Deep_Belief_Networks_Based_Brain_Activity_Classification_Using_EEG_from_Slow_Cortical_Potentials_in_Stroke
    5. \n
  5. uiying Li, Dongxue Zhang, Jingmeng Xie, MI-DABAN: A dual-attention-based adversarial network for motor imagery classification, Computers in Biology and Medicine, Volume 152, 2023, 106420, ISSN 0010-4825,
    \nhttps://doi.org/10.1016/j.compbiomed.2022.106420.
    \n(https://www.sciencedirect.com/science/article/pii/S0010482522011283)
    6. \n
", "messages": [ { "id": 2664504, "postDate": "2024-02-23T04:25:27.893Z", "content": "

Refer to \"HMS: Harmful Brain Activity Classification\" Competition.
\nhttps://www.kaggle.com/competitions/hms-harmful-brain-activity-classification

\n

I am sharing here a list of useful and relevant papers for this competition:

\n
    \n
  1. . Fahmi, V. Aprianti, B. Siregar and M. Aziz, \"Design of Classification of Human Stress Levels Based on Brain Wave Observation Using EEG with K-NN Algorithm,\" 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), Medan, Indonesia, 2022, pp. 200-205, doi: 10.1109/ELTICOM57747.2022.10038004. Web: https://ieeexplore.ieee.org/document/10038004
    2. \n
  2. Caiola M, Babu A, Ye M (2023) EEG classification of traumatic brain injury and stroke from a nonspecific population using neural networks. PLOS Digit Health 2(7): e0000282. https://doi.org/10.1371/journal.pdig.0000282 Web: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000282
    3. \n
  3. Samee NA, Mahmoud NF, Atteia G, Abdallah HA, Alabdulhafith M, Al-Gaashani MSAM, Ahmad S, Muthanna MSA. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel). 2022 Oct 20;12(10):2541. doi: 10.3390/diagnostics12102541. PMID: 36292230; PMCID: PMC9600529. Web: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600529/
    4. \n
  4. Altan, Gokhan & Kutlu, Yakup & Allahverdi, Novruz. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers. 205-205. 10.18100/ijamec.270307. Web: https://www.researchgate.net/publication/311850531_Deep_Belief_Networks_Based_Brain_Activity_Classification_Using_EEG_from_Slow_Cortical_Potentials_in_Stroke
    5. \n
  5. uiying Li, Dongxue Zhang, Jingmeng Xie, MI-DABAN: A dual-attention-based adversarial network for motor imagery classification, Computers in Biology and Medicine, Volume 152, 2023, 106420, ISSN 0010-4825,
    \nhttps://doi.org/10.1016/j.compbiomed.2022.106420.
    \n(https://www.sciencedirect.com/science/article/pii/S0010482522011283)
    6. \n
", "rawMarkdown": "Refer to \"HMS: Harmful Brain Activity Classification\" Competition.\nhttps://www.kaggle.com/competitions/hms-harmful-brain-activity-classification\n\nI am sharing here a list of useful and relevant papers for this competition:\n1. . Fahmi, V. Aprianti, B. Siregar and M. Aziz, \"Design of Classification of Human Stress Levels Based on Brain Wave Observation Using EEG with K-NN Algorithm,\" 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), Medan, Indonesia, 2022, pp. 200-205, doi: 10.1109/ELTICOM57747.2022.10038004. Web: https://ieeexplore.ieee.org/document/10038004\n2. Caiola M, Babu A, Ye M (2023) EEG classification of traumatic brain injury and stroke from a nonspecific population using neural networks. PLOS Digit Health 2(7): e0000282. https://doi.org/10.1371/journal.pdig.0000282 Web: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000282\n3. Samee NA, Mahmoud NF, Atteia G, Abdallah HA, Alabdulhafith M, Al-Gaashani MSAM, Ahmad S, Muthanna MSA. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel). 2022 Oct 20;12(10):2541. doi: 10.3390/diagnostics12102541. PMID: 36292230; PMCID: PMC9600529. Web: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600529/\n4. Altan, Gokhan & Kutlu, Yakup & Allahverdi, Novruz. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers. 205-205. 10.18100/ijamec.270307. Web: https://www.researchgate.net/publication/311850531_Deep_Belief_Networks_Based_Brain_Activity_Classification_Using_EEG_from_Slow_Cortical_Potentials_in_Stroke\n5. uiying Li, Dongxue Zhang, Jingmeng Xie, MI-DABAN: A dual-attention-based adversarial network for motor imagery classification, Computers in Biology and Medicine, Volume 152, 2023, 106420, ISSN 0010-4825,\nhttps://doi.org/10.1016/j.compbiomed.2022.106420.\n(https://www.sciencedirect.com/science/article/pii/S0010482522011283)\n", "votes": -6 } ], "comments": [], "raw_markdown_by_id": { "2664504": "Refer to \"HMS: Harmful Brain Activity Classification\" Competition.\nhttps://www.kaggle.com/competitions/hms-harmful-brain-activity-classification\n\nI am sharing here a list of useful and relevant papers for this competition:\n1. . Fahmi, V. Aprianti, B. Siregar and M. Aziz, \"Design of Classification of Human Stress Levels Based on Brain Wave Observation Using EEG with K-NN Algorithm,\" 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), Medan, Indonesia, 2022, pp. 200-205, doi: 10.1109/ELTICOM57747.2022.10038004. Web: https://ieeexplore.ieee.org/document/10038004\n2. Caiola M, Babu A, Ye M (2023) EEG classification of traumatic brain injury and stroke from a nonspecific population using neural networks. PLOS Digit Health 2(7): e0000282. https://doi.org/10.1371/journal.pdig.0000282 Web: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000282\n3. Samee NA, Mahmoud NF, Atteia G, Abdallah HA, Alabdulhafith M, Al-Gaashani MSAM, Ahmad S, Muthanna MSA. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel). 2022 Oct 20;12(10):2541. doi: 10.3390/diagnostics12102541. PMID: 36292230; PMCID: PMC9600529. Web: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600529/\n4. Altan, Gokhan & Kutlu, Yakup & Allahverdi, Novruz. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers. 205-205. 10.18100/ijamec.270307. Web: https://www.researchgate.net/publication/311850531_Deep_Belief_Networks_Based_Brain_Activity_Classification_Using_EEG_from_Slow_Cortical_Potentials_in_Stroke\n5. uiying Li, Dongxue Zhang, Jingmeng Xie, MI-DABAN: A dual-attention-based adversarial network for motor imagery classification, Computers in Biology and Medicine, Volume 152, 2023, 106420, ISSN 0010-4825,\nhttps://doi.org/10.1016/j.compbiomed.2022.106420.\n(https://www.sciencedirect.com/science/article/pii/S0010482522011283)\n" } }