diff --git a/mne/io/eeglab/eeglab.py b/mne/io/eeglab/eeglab.py
index 7e006275c20..2e7784925a5 100644
--- a/mne/io/eeglab/eeglab.py
+++ b/mne/io/eeglab/eeglab.py
@@ -619,6 +619,38 @@ def __init__(
                 "You should try using read_raw_eeglab function."
             )
 
+        def _generate_boundary_events(n_trials):
+            """
+            Generate boundary events for epoched data without events.
+
+            Parameters
+            ----------
+            n_epochs: int
+                Number of epochs (trials).
+
+            Returns
+            -------
+            events: ndarray
+                Array of boundary events
+            event_id: dict
+                Event id dictionary
+            """
+            events = np.zeros((n_trials, 3), dtype=np.int64)  # Explicitly use int64
+            events[:, 0] = np.arange(
+                n_trials, dtype=np.int64
+            )  # Start from 0 for sample numbers
+            events[:, 1] = 0  # Previous event value
+            events[:, 2] = 1  # Boundary marker
+
+            event_id = {"boundary": 1}
+
+            # Double-check the array type
+            assert (
+                events.dtype == np.int64
+            ), f"Events dtype is {events.dtype}, expected np.int64"
+
+            return events, event_id
+
         if events is None and eeg.trials > 1:
             # first extract the events and construct an event_id dict
             event_name, event_latencies, unique_ev = list(), list(), list()
@@ -656,15 +688,21 @@ def __init__(
                 )
 
             # now fill up the event array
-            events = np.zeros((eeg.trials, 3), dtype=int)
-            for idx in range(0, eeg.trials):
-                if idx == 0:
-                    prev_stim = 0
-                elif idx > 0 and event_latencies[idx] - event_latencies[idx - 1] == 1:
-                    prev_stim = event_id[event_name[idx - 1]]
-                events[idx, 0] = event_latencies[idx]
-                events[idx, 1] = prev_stim
-                events[idx, 2] = event_id[event_name[idx]]
+            if event_id is None and not event_name and not event_latencies:
+                # account for EEGLAB with trials but no events
+                events, event_id = _generate_boundary_events(eeg.trials)
+            else:
+                events = np.zeros((eeg.trials, 3), dtype=int)
+                for idx in range(0, eeg.trials):
+                    if idx == 0:
+                        prev_stim = 0
+                    elif (
+                        idx > 0 and event_latencies[idx] - event_latencies[idx - 1] == 1
+                    ):
+                        prev_stim = event_id[event_name[idx - 1]]
+                    events[idx, 0] = event_latencies[idx]
+                    events[idx, 1] = prev_stim
+                    events[idx, 2] = event_id[event_name[idx]]
         elif isinstance(events, (str, Path, PathLike)):
             events = read_events(events)