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6 followers · 3 following

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Achievement: Pull Sharkx2

Achievements

Achievement: Pull Sharkx2

Organizations

@affctivai@affctivai-urp

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bhyungk/README.md

👋 Byung Hyung Kim (김병형)

I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.

In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.

🔬 Current Research Interests

  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning

🌐 AFFCTIV Lab

📫 Contact If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!

"Emotion-aware intelligence is the future of human-AI interaction."

Pinned Loading

  1. affctivai/CCMTLaffctivai/CCMTLPublic

    Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python 13

  2. affctivai/coglieraffctivai/coglierPublic

    Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python 1 2

  3. affctivai/ConTLaffctivai/ConTLPublic

    Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python 2 1

  4. affctivai/MORaffctivai/MORPublic

    Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook 1 1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic

    RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python 11 2

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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6
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Achievement: Pull Sharkx2
Achievements
Achievement: Pull Sharkx2
Organizations
@affctivai@affctivai-urp

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

Pinned
Loading
  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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6
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 · 3
following

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Achievement: Pull Sharkx2
Achievements
Achievement: Pull Sharkx2
Organizations
@affctivai@affctivai-urp

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

Pinned
Loading
  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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6
followers
 · 3
following

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Achievement: Pull Sharkx2
Achievements
Achievement: Pull Sharkx2
Organizations
@affctivai@affctivai-urp

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

Pinned
Loading
  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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6
followers
 · 3
following

Achievements
Achievement: Pull Sharkx2
Achievements
Achievement: Pull Sharkx2
Organizations
@affctivai@affctivai-urp

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

Pinned
Loading
  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View bhyungk's full-sized avatar
6
followers
 · 3
following

Achievements
Achievement: Pull Sharkx2
Achievements
Achievement: Pull Sharkx2
Organizations
@affctivai@affctivai-urp

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

Pinned
Loading
  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View bhyungk's full-sized avatar
6
followers
 · 3
following

Achievements
Achievement: Pull Sharkx2
Achievements
Achievement: Pull Sharkx2
Organizations
@affctivai@affctivai-urp

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

Pinned
Loading
  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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bhyungk/README.md
👋 Byung Hyung Kim (김병형)
I am an Assistant Professor in the Department of Artificial Intelligence at Inha University, where I lead the Affective Artificial Intelligence (AFFCTIV) Lab. My research lies at the intersection of brain-computer interfaces, affective computing, and deep learning, with a particular focus on understanding and modeling human cognitive and emotional states using multimodal biosignals such as EEG, fNIRS, and facial expressions.
In our lab, we explore novel AI models that are not only effective but also emotionally and cognitively aware. Our goal is to build intelligent systems that understand humans better — both in theory and in real-world applications.
🔬 Current Research Interests
  • Affective computing & emotion recognition
  • Brain-computer interface (BCI)
  • Geometric deep learning on Riemannian manifolds
  • Generative models for neural decoding
  • Multimodal signal fusion and robust representation learning
🌐 AFFCTIV Lab
📫 Contact
If you're passionate about AI that understands the human mind and emotion, feel free to explore our work or reach out!
"Emotion-aware intelligence is the future of human-AI interaction."

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  1. affctivai/CCMTLaffctivai/CCMTLPublic
    
Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

    Python
13

  2. affctivai/coglieraffctivai/coglierPublic
    
Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

    Python
1

2

  3. affctivai/ConTLaffctivai/ConTLPublic
    
Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

    Python
2

1

  4. affctivai/MORaffctivai/MORPublic
    
Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

    Jupyter Notebook
1

1

  5. affctivai/RecSal-Netaffctivai/RecSal-NetPublic
    
RecSal-Net: Recursive Saliency Network for Video Saliency Prediction

    Python
11

2