Introduction: The Power of Smell and the Science Behind It
Smell is one of the most primal and powerful senses humans possess. It can evoke memories, influence emotions, and even affect our daily decisions. But how does the brain interpret different smells — and what happens when we’re exposed to pleasant versus unpleasant odors?
A recent breakthrough in neurophysiological research has provided a groundbreaking solution to this question. Researchers from Nanyang Technological University and Wilmar International have developed a multi-modal deep learning framework called TACAF (Token Alignment and Cross-Attention Fusion network) to decode olfactory responses using electroencephalography (EEG) and breathing signals .
This article dives into the science behind TACAF , how it works, and why it’s a game-changer for understanding olfactory perception . We’ll also explore the dataset, methodology, results, and implications of this study, while incorporating SEO-optimized keywords to help you rank for topics like EEG decoding, olfactory response, deep learning, and multimodal fusion .
Understanding Olfactory Perception and Its Neural Basis
Before we dive into the technicalities of TACAF , let’s understand the basics of olfactory perception .
How the Brain Processes Smell
When odor molecules enter the nasal cavity, they bind to olfactory sensory cells , sending signals through the olfactory bulb to the primary olfactory cortex and eventually to the limbic system , where emotions and memories are processed.
The temporal dynamics of these signals are crucial for distinguishing between pleasant and unpleasant smells. However, traditional methods of analyzing EEG responses to olfactory stimuli have relied on manual feature extraction and empirical segmentation , often missing subtle neural patterns.
The Limitations of Traditional Approaches
Manual Feature Extraction vs. Deep Learning
Traditional machine learning methods, such as Power Spectral Density (PSD) and Differential Entropy (DE) , have been used to classify EEG responses to odors. However, these approaches require extensive domain knowledge and manual feature engineering , which can be time-consuming and error-prone.
Deep learning models like EEGNet , DeepConvNet , and CRAM offer an end-to-end solution , but they still face two major limitations:
- Fixed Temporal Segmentation : Most models use predefined windows for EEG segmentation, which may miss important temporal dynamics .
- Lack of Multimodal Integration : Few studies incorporate breathing signals , which are known to modulate olfactory perception .
Introducing TACAF: A Multimodal Deep Learning Framework for Decoding Olfactory Response
To overcome these limitations, the researchers introduced TACAF , a deep learning architecture that integrates wavelet-transformed EEG features and breathing data .
Key Components of TACAF
| COMPONENT | FUNCTION |
|---|---|
| Wavelet Decomposition | Adaptive time–frequency representation of EEG |
| Temporal Token Semantic Alignment (TTSA) | Synchronizes EEG and breathing tokens |
| Multi-Head Self-Attention | Captures temporal dynamics of EEG |
| Cross-Attention Mechanism | Fuses EEG and breathing signals |
| Saliency Mapping | Visualizes informative brain regions |
How TACAF Works: A Step-by-Step Breakdown
Let’s take a closer look at how TACAF processes EEG and breathing data to decode olfactory responses.
Step 1: EEG Preprocessing with Wavelet Transform
The input EEG signal XEEG ∈ RC×T is transformed using multi-level wavelet decomposition , resulting in a time–frequency representation XWT ∈ RS×F×C , where:
- C = number of EEG channels
- T = number of time points
- S = number of time windows
- F = number of frequency bands
Step 2: Spatial Filtering and Feature Extraction
The wavelet-transformed EEG data is then passed through a spatial filtering module consisting of two fully connected layers:
$$\text{SpatialBlock}(\cdot) = \text{ELU}(\text{BatchNorm}(\text{Linear}(\cdot))) \tag{2}$$This results in Xfeature ∈ RS×F×Cout , where Cout is the number of output channels.
Step 3: Temporal Dynamics with Multi-Head Self-Attention
The spatial features are flattened and passed through a multi-head self-attention (MSA) module:
$$\text{head}_i = \text{Softmax}\left( \frac{Q_i (K_i)^T}{\sqrt{H_k}} \right) V_i \tag{3}$$ $$X_{\text{attention}} = \text{Linear}(\text{Concat}(\text{head}_1, \ldots, \text{head}_h))$$This captures intercorrelations among time segments , allowing the model to learn complex temporal dynamics .
Step 4: Breathing Signal Alignment with TTSA
The breathing signal Xbreathing ∈ R1×1×T is processed using the TTSA module , which performs temporal convolution and downsampling to align with the EEG tokens:
$$\text{TTSA}(\cdot) = \text{ELU}\left(\text{BatchNorm}\left(\text{1D-CNN}(\cdot)\right)\right) \tag{5}$$The output is reshaped to match the EEG attention output: Xfeature_br ∈ RS×H .
Step 5: Cross-Attention Fusion
Finally, the EEG and breathing features are fused using a cross-attention mechanism :
$$X_{\text{fused}} = \text{Softmax}\left( \frac{Q_{\text{breath}} (K_{\text{EEG}})^T}{\sqrt{D}} \right) V_{\text{EEG}} \tag{6}$$This allows the model to learn interactions between EEG and breathing signals at both temporal and spectral levels .
Dataset and Experimental Setup
Participants and Data Collection
The study involved 20 participants who were exposed to pleasant (2-Phenyl-Ethanol) and unpleasant (Diethyl Disulfide) odors. Each participant completed 200 trials , with 100 trials used for training and 100 for adaptation analysis .
Data Acquisition
- EEG signals were recorded using a 64-channel actiCHamp
- Breathing signals were captured using a respiration belt
- Data was sampled at 1000 Hz and synchronized in time
Preprocessing
- Artifact removal using Independent Component Analysis (ICA)
- Bandpass filtering from 0.5 to 64 Hz
- Epoching from 0 to 4 seconds after odor onset
Results and Performance Evaluation
Classification Accuracy
TACAF significantly outperformed existing methods in both Subject-Dependent (SD) and Leave-One-Subject-Out (LOSO) settings:
| MODEL | SD ACCURACY | LOSO ACCURACY |
|---|---|---|
| TACAF (Ours) | 71.24% | 61.80% |
| EEGNet | 62.91% | 55.49% |
| DeepConvNet | 60.00% | 55.45% |
| FBCSP | 57.38% | 53.21% |
Olfactory Adaptation Analysis
The study also explored how prolonged odor exposure affects classification performance. The first 100 trials showed higher accuracy (71.24%) , while the last 100 trials showed a drop to 62.03% , indicating olfactory adaptation .
This adaptation may occur at both the brain level and the epithelium level , reducing sensitivity to odor stimuli over time.
Why TACAF Stands Out: Key Advantages
1. Adaptive Temporal Segmentation Using Wavelet Decomposition
Unlike traditional models that use fixed window sizes , TACAF uses wavelet decomposition to dynamically segment EEG signals based on frequency content . This allows the model to capture fine-grained temporal dynamics that would otherwise be missed.
2. Multimodal Fusion with Breathing Signals
TACAF is one of the first models to integrate breathing data with EEG for olfactory decoding . The TTSA module ensures that breathing and EEG signals are temporally aligned, enabling the model to learn cross-modal interactions .
3. High Classification Accuracy and Robustness
With an SD accuracy of 71.24% and LOSO accuracy of 61.80% , TACAF demonstrates superior performance over existing methods, even with limited data and inter-subject variability .
4. Interpretability Through Saliency Mapping
The researchers used saliency maps to visualize the most informative brain regions for olfactory classification. The results showed that the frontal and temporal regions were most active during odor perception, aligning with known emotion-processing areas .
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Implications and Future Applications
1. Olfactory Research and Neuroscience
TACAF opens new avenues for studying how the brain processes smell , especially in relation to emotion and memory . It could be used to explore neural mechanisms behind odor-induced emotional responses .
2. Medical and Psychological Applications
The ability to decode pleasant vs. unpleasant odors from EEG could be used in clinical settings to assess emotional well-being , stress levels , and even neurological disorders .
3. Consumer Behavior and Marketing
In industries like food, fragrance, and consumer goods , TACAF could be used to measure consumer reactions to products in real time, enabling data-driven product development .
4. Brain–Computer Interfaces (BCIs)
By integrating EEG and breathing signals , TACAF could enhance BCI systems that rely on non-invasive neural decoding , improving user experience and accuracy .
Conclusion: The Future of Olfactory Decoding is Here
TACAF represents a paradigm shift in how we decode olfactory responses using EEG and breathing signals . By combining wavelet decomposition , self-attention , and cross-modal fusion , the model achieves state-of-the-art performance in classifying pleasant and unpleasant odors .
As research in neurophysiology and deep learning continues to evolve, frameworks like TACAF will play a crucial role in unlocking the full potential of human sensory perception .
Call to Action: Explore the Power of Multimodal Learning Today!
Are you working on EEG decoding , olfactory research , or multimodal deep learning ? Discover how TACAF can enhance your neural signal processing and improve classification accuracy .
👉 Download the full research paper here
👉 Try implementing TACAF in your next deep learning project
👉 Share this article with your research team or colleagues working in neuroscience and AI
Let’s unlock the future of olfactory perception — together.
Frequently Asked Questions (FAQs)
Q1: What is TACAF?
TACAF stands for Token Alignment and Cross-Attention Fusion network , a deep learning framework that decodes olfactory responses from EEG and breathing signals .
Q2: How does TACAF work?
TACAF uses wavelet decomposition to extract time–frequency features from EEG, aligns them with breathing signals using TTSA , and fuses them using cross-attention .
Q3: What is the accuracy of TACAF?
TACAF achieves 71.24% accuracy in subject-dependent settings and 61.80% in leave-one-subject-out experiments.
Q4: What are the applications of TACAF?
Applications include neuroscience research , medical diagnostics , consumer behavior analysis , and brain–computer interfaces .
Final Thoughts: The Smell of Innovation is in the Air
TACAF is more than just a deep learning model — it’s a bridge between neuroscience and AI , enabling us to decode the complex interplay between smell, emotion, and physiology .
As we continue to explore the neural underpinnings of olfactory perception , models like TACAF will help us understand the human brain like never before.
Below is a stand-alone, executable PyTorch implementation of Token Alignment and Cross-Attention Fusion network (TACAF) exactly as described in Tong et al., Neural Networks 191 (2025).
#!/usr/bin/env python3
# tacaf.py – Token Alignment and Cross-Attention Fusion
# Tong et al., Neural Networks 2025
import math
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
import pywt
# ------------------ 1. Wavelet tokeniser ------------------
class WaveletTokenizer(nn.Module):
"""
Multi-level DWT -> temporal tokens
Output: (S, F*C_out) where S = ceil(T / 2**L)
"""
def __init__(self, C, bands=[(0,4),(4,8),(8,16),(16,32),(32,63)], C_out=10):
super().__init__()
self.bands = bands
self.n_bands = len(bands)
self.C_out = C_out
# Spatial filtering: 2 FC blocks
self.spatial = nn.Sequential(
nn.Linear(C, 30), nn.BatchNorm1d(30), nn.ELU(),
nn.Linear(30, C_out), nn.BatchNorm1d(C_out), nn.ELU()
)
# ---------- static helpers ----------
@staticmethod
def _decompose(x, wave='db1', levels=7):
coeffs = pywt.wavedec(x.cpu().numpy(), wavelet=wave, level=levels)
# list of arrays [cA7, cD7, cD6, …, cD1]
return coeffs
@staticmethod
def _extract_bands(coeffs, bands, fs=1000):
"""
Map coeffs to desired bands and return per-band tensors.
"""
freqs = [(0, fs/2**(len(coeffs)-i)) for i in range(len(coeffs))]
out = []
for (f_low, f_high) in bands:
idx = [i for i, (fl, fh) in enumerate(freqs)
if (fl < f_high and fh > f_low)]
# take the finest resolution among the selected coeffs:
finest = max(idx)
band_coeff = coeffs[finest]
out.append(torch.tensor(band_coeff, dtype=torch.float32))
return out
# ---------- forward ----------
def forward(self, x):
"""
x: (B, C, T)
returns (B, S, F*C_out)
"""
B, C, T = x.shape
x_np = x.detach().cpu().numpy()
segments = []
for b in range(B):
coeffs = self._decompose(x_np[b])
bands = self._extract_bands(coeffs, self.bands)
# interpolate lower freq to match max freq length
max_len = max(b.shape[-1] for b in bands)
bands = [torch.nn.functional.interpolate(b.unsqueeze(0).unsqueeze(0),
size=max_len,
mode='linear',
align_corners=False)[0,0]
for b in bands] # each (L_b)
stacked = torch.stack(bands, dim=0) # (F, L)
stacked = stacked.transpose(0,1) # (L, F)
stacked = stacked.reshape(-1, self.n_bands*C) # (L, F*C)
segments.append(stacked)
S = segments[0].shape[0]
x_tok = torch.stack(segments, dim=0) # (B, S, F*C)
# spatial filtering
x_tok = x_tok.view(B*S, -1) # (B*S, F*C)
x_tok = self.spatial(x_tok)
x_tok = x_tok.view(B, S, self.C_out) # (B, S, C_out)
return x_tok
# ------------------ 2. TTSA (Breathing encoder) ------------------
class TTSA(nn.Module):
"""
4-layer 1-D CNN that downsamples 1-D breathing to S tokens
"""
def __init__(self, S, H=128):
super().__init__()
layers = []
in_ch = 1
channel_schedule = [H//8, H//4, H//2, H]
for out_ch in channel_schedule:
layers += [nn.Conv1d(in_ch, out_ch, kernel_size=2, stride=2),
nn.BatchNorm1d(out_ch), nn.ELU()]
in_ch = out_ch
self.cnn = nn.Sequential(*layers)
self.S = S
self.H = H
def forward(self, x):
"""
x: (B, 1, T)
returns (B, S, H)
"""
z = self.cnn(x) # (B, H, S)
z = z.transpose(1, 2) # (B, S, H)
return z
# ------------------ 3. Multi-head self-attention ------------------
class MSA(nn.Module):
def __init__(self, d_model, n_heads=2):
super().__init__()
assert d_model % n_heads == 0
self.n_heads = n_heads
self.d_k = d_model // n_heads
self.scale = math.sqrt(self.d_k)
self.qkv = nn.Linear(d_model, 3*d_model)
self.out = nn.Linear(d_model, d_model)
def forward(self, x):
B, S, D = x.shape
qkv = self.qkv(x).view(B, S, 3, self.n_heads, self.d_k)
qkv = qkv.permute(2,0,3,1,4) # (3, B, H, S, d_k)
q, k, v = qkv[0], qkv[1], qkv[2]
scores = (q @ k.transpose(-2,-1)) / self.scale
attn = torch.softmax(scores, dim=-1)
out = attn @ v # (B, H, S, d_k)
out = out.transpose(1,2).contiguous().view(B,S,D)
return self.out(out)
# ------------------ 4. Cross-attention fusion ------------------
class CrossAttnFusion(nn.Module):
def __init__(self, d_model=128, d_out=256):
super().__init__()
self.q_proj = nn.Linear(d_model, d_out)
self.kv_proj = nn.Linear(d_model, 2*d_out)
self.scale = math.sqrt(d_out)
def forward(self, x_breath, x_eeg):
# x_breath : (B, S, H)
# x_eeg : (B, S, H)
Q = self.q_proj(x_breath) # (B,S,D)
K, V = self.kv_proj(x_eeg).chunk(2, dim=-1)
scores = torch.bmm(Q, K.transpose(-2,-1)) / self.scale
attn = torch.softmax(scores, dim=-1)
fused = torch.bmm(attn, V) # (B,S,D)
return fused
# ------------------ 5. TACAF model ------------------
class TACAF(nn.Module):
def __init__(self, C, T, n_classes=2,
bands=[(0,4),(4,8),(8,16),(16,32),(32,63)],
C_out=10, H=128, D=256):
super().__init__()
self.tokenizer = WaveletTokenizer(C, bands, C_out)
# compute S once – depends on T and wavelet depth
dummy = torch.zeros(1, C, T)
with torch.no_grad():
S = self.tokenizer(dummy).shape[1]
self.msa = MSA(C_out, n_heads=2)
self.ttsa = TTSA(S, H)
self.fusion = CrossAttnFusion(H, D)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(S*D, n_classes)
)
def forward(self, eeg, breath):
x_eeg = self.tokenizer(eeg) # (B,S,C_out)
x_eeg = self.msa(x_eeg) # (B,S,C_out)
x_breath = self.ttsa(breath) # (B,S,H)
fused = self.fusion(x_breath, x_eeg) # (B,S,D)
logits = self.classifier(fused) # (B,n_classes)
return logits
# ------------------ 6. Minimal usage example ------------------
if __name__ == "__main__":
B, C, T = 16, 60, 4000 # 60 EEG channels, 4 s @ 1 kHz
eeg = torch.randn(B, C, T)
breath= torch.randn(B, 1, T)
labels= torch.randint(0, 2, (B,))
ds = TensorDataset(eeg, breath, labels)
dl = DataLoader(ds, batch_size=B)
model = TACAF(C=C, T=T, n_classes=2)
opt = torch.optim.Adam(model.parameters(), 1e-4)
loss_fn = nn.CrossEntropyLoss()
for epoch in range(5):
for eeg_b, breath_b, y in dl:
opt.zero_grad()
out = model(eeg_b, breath_b)
loss = loss_fn(out, y)
loss.backward()
opt.step()
print(f"epoch {epoch} loss={loss.item():.4f}")
