Crafting immersive neural experiences through data, sound, and human-centered design.
const navaneeth = {
identity: {
name: "Navaneeth K",
location: "India 🇮🇳",
status: "Early-Career Computational Neuroscience Researcher"
},
research: {
primary: "Brain-Computer Interfaces (BCI) & Neurotech",
focus: [
"Neural Signal Processing & EEG Analysis",
"Audio-Brain Interface Integration",
"Real-time BCI Applications",
"Computational Neuroscience Models"
],
interests: [
"Cognitive Neuroscience",
"Neural Decoding Algorithms",
"Closed-loop Brain Stimulation"
]
},
engineering: {
domains: ["IoT Systems", "Full-Stack Development", "Audio Technology"],
exploring: ["Edge Computing", "Neural Data Processing", "Embedded Systems"],
},
philosophy: {
mission: "Building brain-responsive systems for next-gen human-computer interaction",
vision: "Democratizing neural interfaces through accessible innovation",
approach: "Bridging neuroscience, technology, and creative problem-solving"
},
currentWork: [
"🎧 Audio-driven BCI applications",
"🧠 EEG signal processing pipelines",
"🌐 IoT-integrated neural monitoring systems",
"💻 Full-stack neurotech tools"
],
learning: ["Advanced ML for Neuroscience", "Hardware-Software Co-Design", "Signal Processing"],
openTo: "Research collaborations, innovative projects, and interdisciplinary discussions"
};
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📖 Key BCI Concepts & Methodologies
Signal Acquisition & Preprocessing
- EEG electrode placement (10-20 system)
- Artifact removal (EOG, EMG filtering)
- Bandpass filtering & ICA decomposition
- Feature extraction (spectral, time-domain)
Machine Learning Pipeline
- Classification algorithms (LDA, SVM, Neural Networks)
- Cross-validation & hyperparameter tuning
- Real-time prediction & feedback loops
Applications in Development
- 🎧 Audio-responsive BCI for music interaction
- 🧠 Cognitive state monitoring systems
- 🌐 IoT-integrated neural interfaces
- 💻 Assistive communication devices
class ResearchJourney:
def __init__(self):
self.active_research = {
"primary": "Neural correlates of audio perception",
"experiments": [
"Real-time EEG analysis during music listening",
"BCI control using auditory attention",
"IoT-neural monitoring integration"
]
}
self.skill_development = [
"Advanced signal processing (wavelet transforms, time-frequency analysis)",
"Deep learning for neural decoding (CNNs, RNNs, Transformers)",
"Hardware interfacing (OpenBCI, custom electrodes)",
"Real-time systems design & optimization"
]
self.building = [
"🎧 Audio-BCI experimental platform",
"📊 Neural data visualization toolkit",
"🌐 Full-stack neurotech web applications",
"🛠️ Open-source signal processing pipelines"
]
self.goals = {
"short_term": "Publish BCI research findings",
"medium_term": "Graduate school (computational neuroscience)",
"long_term": "Advance democratized neural interfaces"
}
self.open_to = [
"Research collaborations",
"Neurotech project partnerships",
"Academic discussions & mentorship"
]|
Neural signal processing |
EEG/fNIRS systems |
Auditory neuroscience |
💡 Open to research collaborations, neurotech projects, and innovative ideas